This is the generic class for apparatus and corresponding
methods for constructing, analyzing, and modifying units
of human language by data processing, in
which there is a significant change in the data.
This class also provides for systems or methods that process speech signals for storage, transmission, recognition, or
synthesis of speech.
This class also provides for systems or methods for bandwidth
compression or expansion of an audio signal, or for time
compression or expansion of an audio signal.
Class 704 is structured into three main divisions:
A. Linguistics.
B. Speech Signal Processing.
C. Audio Compression.
See Subclass References to the Current Class, below, for the
subclasses located within each of these three main divisions.
SECTION II - LINES WITH OTHER CLASSES AND WITHIN THIS CLASS
A. LINGUISTICS
1. This class does not include subject matter wherein significant
details of the modification or construction of documents are claimed. (See
Class ?0? in the Search Class notes below in References
to Other Classes, regarding Document Processing).
2. This class does not include subject matter directed
to significant details of teaching languages. (See
Class 434 in the Search Class notes in References to Other Classes, below).
3. This class does not include subject matter directed
to significant details of the construction, analysis or
modification of computer languages. (See
Class 717 in the Search Class notes in References to Other Classes, below).
B. IMAGE ANALYSIS
1. This class does not include subject matter wherein significant
image analysis is performed and speech signal
processing is nominally claimed (see Class 382 in the Search
Class notes in References to Other Classes, below).
2. This class includes subject matter directed to speech signal processing disclosed or
claimed in plural diverse arts such as image analysis (classified, per
se, in Class 382).
C. AUDIO SIGNAL PROCESSING
1. This class does not include subject matter wherein nominal
bandwidth or time modifications are performed for other audio processing
defined in Classes 381 or 84 (see Search Class notes below
in References to Other Classes). Examples of
subject matter not included are: Stereo, sound
effects, hearing aids, input and output transducers, and
musical instruments.
2. This class includes audio signal processing wherein significant
processing is performed to modify the signal"s bandwidth or time
characteristics for compression or expansion of the signal.
D. COMMUNICATIONS
1. This class does not include subject matter wherein significant
details of a distinct communications system or telephone link is
performed and speech signal processing
is nominally claimed (see Classes 340, 370, 375, 379, 455
in the Search Class notes below in References to Other Classes.).
2. This class includes subject matter directed to speech signal processing disclosed or
claimed in plural diverse arts such as various types of communication
systems.
E. APPLICATIONS
1. This class does not include subject matter wherein significant
details of application systems are performed and speech signal
processing is nominally claimed.
2. This class includes subject matter directed to speech signal processing disclosed or
claimed in plural diverse arts to include electrical and mechanical
systems. Examples would include systems controlled by speech recognition, systems which
create specific displays of speech data, systems
for editing speech data and otherwise
unrelated systems which incorporate speech signal processing
details such as placing a speech synthesizer into
novelty items.
SECTION III - SUBCLASS REFERENCES TO THE CURRENT CLASS
Music,
subclasses 1+ for instruments used in producing music to include (a) electrical
music instruments, (b) automatic instruments, and (c) hand-played
instruments. Automatic and hand-played instruments
are divided into four groups: stringed, wind, rigid
vibrators, and membranes. This class also includes
some accessory devices generally recognized as belonging to the
art or industry.
Acoustics, various subclasses, for mechanically transmitting, amplifying
and ascertaining the direction of sound and for mechanically muffling
or filtering sound.
Communications: Electrical,
subclasses 1.1 through 16.1for controlling one or more devices to obtain a
plurality of results by transmission of a designated one of plural
distinctive control signals over a smaller number of communication
lines or channels.
Coded Data Generation or Conversion, various subclasses for electrical pulse and digit code
converters (e.g., systems for
originating or emitting a coded set of discrete signals or translating
one code into another code wherein the meaning of the data remains
the same but the formats may differ).
Computer Graphics Processing and Selective Visual
Display Systems, various subclasses for the selective control of two or
more light generating or light controlling display elements in accordance
with a received image signal, and
subclasses 1.1 through 3.4for visual display systems with selective electrical
control including display memory organization and structure for
storing image data and manipulating image data between a display
memory and display device.
Dynamic Magnetic Information Storage or Retrieval, which is an integral part of Class 369 following
subclass 18 , for record carriers and systems wherein
information is stored and retrieved by interaction with a medium
and there is relative motion between a medium and a transducer, for
example, magnetic disk drive devices, and control
thereof, per se.
Static Information Storage and Retrieval, various subclasses for addressable static singular storage
elements or plural singular storage elements of the same type (i.e., the
internal elements of memory, per se).
Dynamic Information Storage or Retrieval, various subclasses for record carriers and systems
wherein information is stored and retrieved by interaction with
a medium and there is relative motion between a medium and a transducer.
Multiplex Communications, for the simultaneous transmission of two or more signals
over a common medium, particularly
subclasses 58.1+ for time division multiplex (TDM) switching, subclasses
85.1+ for time division bus transmission, and
subclasses 91+ for asynchronous TDM communications including addressing.
Pulse or Digital Communications, various subclasses for generic pulse or digital
communication systems and synchronization of clocking signals from
input data.
Telephonic Communications, various subclasses for two-way electrical
communication of intelligible audio information of arbitrary content
over a link including an electrical conductor.
Image Analysis, various subclasses for operations performed on image
data with the aim of measuring a characteristic of an image, detecting
variations, detecting structures, or transforming
the image data, and for procedures for analyzing and categorizing
patterns present in image data.
Education and Demonstration,
subclasses 112+ for communication aids for the handicapped, subclasses
156+ for education and demonstration of language, subclasses
322+ for question or problem eliciting response.
Telecommunications, appropriate subclasses for modulated carrier wave communication, per
se, and
subclass 26.1 for subject matter which blocks access to a signal
source or otherwise limits usage of modulated carrier equipment.
Data Processing: Generic Control Systems
or Specific Applications,
subclasses 1 through 89for data processing generic control systems, subclasses
90-306 for applications of computers in various environments.
Data Processing: Measuring, Calibrating, or Testing, appropriate subclasses for the application of computer
data processing in measuring, calibrating, or
testing.
Electrical Computers: Arithmetic Processing and
Calculating,
subclasses 1+ for hybrid computers, subclasses 100+ for
calculators, digital signal processing and arithmetical
processing, per se, subclasses 300+ for
digital filters, and subclasses 800+ for electric
analog computers.
Electrical Computers and Digital Processing Systems: Support,
subclass 187 and 188 for software program protection or computer
virus detection in combination with data encryption.
Error Detection/Correction and Fault
Detection/Recovery, various subclasses for generic electrical pulse
or pulse coded data error detection and correction.
Data Processing: Presentation Processing
of Document, Operator Interface Processing, and Screen
Saver Display Processing,
subclasses 243 through 272for document processing including layout, editing, and
spell-checking.
Data Processing: Software Development, Installation, and
Management, appropriate subclasses for significant details of
the construction, analysis, or modification of
computer languages.
SECTION V - GLOSSARY
The terms below have been defined for purposes of
classification in this class and are shown in underlined
type when used in the class and subclass definitions. When these
terms are not underlined in the definitions, the meaning
is not restricted to the glossary definitions below.
CORRELATION
A statistical measurement of the interdependence or association
between two variables that are quantitative or qualitative in nature.
A typical calculation would be performed by multiplying a signal
by either another signal (cross-correlation) or
by a delayed version of itself (autocorrelation).
DISTANCE
A statistical measurement for comparing elements defined
by variables or vectors using scalar or vector subtraction of those
elements. Examples: distance=a-b, |a-b|, (a-b).5
or two vectors may be treated as objects such that the straight
line distance is measured between them.
EXCITATION
Stimulation of the vocal tract by vibratory action of
the vocal cords or by a turbulent air flow. In a digital
system, the vocal tract is typically modelled with a filter and excitation of the filter is performed
using time representations of pitch (voicedexcitation) and noise (unvoicedexcitation).
LANGUAGE
A systematic means of communicating ideas or feelings by
the use of conventionalized sounds, gestures, or marks
having understood meanings.
LINGUISTICS
The study of human speech including
the units, nature, structure, and modification
of language.
Masking
1. The interference with the perception of one
sound (the signal) with another sound (the
masker). 2. The number of decibels by
which a masking sound will raise (or change) a
listener"s threshold of audibility of other sounds.
Critical bandwidths
Bandwidths of the hearing process, as measured
by the masking effect of a white, random noise in which
a person detects a pure tone.
Bark spectrum
The width of one critical band.
Mel
A subjective measure of pitch based upon a signal of 1000
Hz. being defined as "1000 mels" where a perceived frequency
twice as high is defined as 2000 mels and half as high as 500 mels.
NOISE
Any sound which is undesirable and interferes with one"s
hearing or with a system"s analysis of desired sound.
Phon
The loudness level of any other sound based upon the SPL (sound
pressure level measured in decibels) of a 1 kHz tone.
For example, if we judge a certain waveform to sound as
loud as a 1 kHz tone at 70 dB, then this waveform has a
loudness level of 70 phons.
PITCH
The measurable frequency or period at which the glottis vibrates.
SIMILARITY
A statistical measurement which is inversely proportional
to distance. For example, if
two patterns are compared yielding a small distance, then
the patterns would exhibit a large (or high degree of) similarity.
Sone
A measure of loudness as a function of frequency and sound
pressure. A pure tone of 1 kHz. at 40 db above
a normal listener"s threshold produces a loudness of 1 sone.
SPEECH
The communication or expression of thoughts in spoken words.
UNVOICED
Speech sounds produced
by a turbulent flow of air created at some point of stricture in
the vocal tract and usually lacking pitch.
VOICED
Speech sounds produced
by vibratory action of the vocal cords and usually having pitch.
This subclass is indented under the class definition. Subject matter including means or steps for constructing
a word, a phrase, or a sentence in a language.
This subclass is indented under subclass 1. Subject matter wherein a language (i.e., source language) stored in a memory
means is translated into another language (i.e., target language).
Computer-Aided Design and Analysis of
Circuits and Semiconductor Masks,
subclasses 103 through 105for translation of computer program in designing
and analyzing circuits and semiconductor mask.
Data Processing: Software Development, Installation, and
Management,
subclasses 136 through 161for software program code translator or compiler
in software development.
This subclass is indented under subclass 2. Subject matter wherein the translation machine includes
a means for reading into the memory means a language, for
pronouncing the translated language or
a particular user interface.
(1)
Note. Examples of such devices include an optical
scanner or voice synthesizer.
This subclass is indented under subclass 2. Subject matter wherein the translation machine includes
a means for providing translation for a specified portion of a sentence
or a clause.
This subclass is indented under subclass 2. Subject matter wherein the translation machine translates
a compound word formed by hyphenation or sentences with quotation marks, colons, semicolons, or
parentheses.
This subclass is indented under subclass 2. Subject matter including a means for assigning storage locations
or accessing addresses to the memory means.
Data Processing: Database, Data
Mining, and File Management or Data Structures
subclasses 736 through 757for preparing data for information retrieval including
clustering, generating an index, ranking, scoring and
weighting records, latent semantic indexing, subclass
760 for translating queries between languages and 794 for semantic
network data structures.
This subclass is indented under subclass 1. Subject matter including means or steps to adapt to, process, or
support plural languages in systems
or in software (i.e., providing language identifiers on files or providing
screen prompts in a selected language), or
to support the conventions or peculiarities of various national languages (i.e., alphabetical
ordering, date or currency indications).
Data Processing: Presentation Processing
of Document, Operator Interface Processing, and
Screen Saver Display Processing,
subclasses 264 through 265for composing or editing multiple languages in
a document and subclass 866 for customization or edition of operator
interfaces.
This subclass is indented under subclass 1. Subject matter includes a means for applying grammatical
rules or other analyses (e.g., morphemic, syntax, semantic, etc.) to
define the true meaning of a sentence or phrase.
(1)
Note. When words are undefined in the dictionary
of a natural language, the grammatical
rules or other analyses are applied in order to determine the true meaning
of a sentence or a phrase.
Data Processing: Database, Data
Mining, and File Management or Data Structures,
subclasses 736 through 757for preparing data for information retrieval including
clustering, generating an index, ranking, scoring and
weighting records, latent semantic indexing, subclass
760 for translating queries between languages and 794 for semantic
network data structures.
This subclass is indented under subclass 1. Subject matter including a construction, a change, or
an orderly arrangement of dictionary, thesauri, or
the like.
Data Processing: Database, Data
Mining, and File Management or Data Structures,
subclasses 736 through 757for preparing data for information retrieval including
clustering, generating an index, ranking, scoring, weighting
records and database details of dictionaries.
Data Processing: Presentation Processing
of Document, Operator Interface Processing, and
Screen Saver Display Processing,
subclasses 259 through 260for mere use of a dictionary in editing or composition
of a document.
This subclass is indented under the class definition. Subject matter wherein the system performs operations or
functions on signals which represent speech.
This subclass is indented under subclass 200. Subject matter wherein an operation on the signal is based
upon the masking behavior of the human auditory system.
(1)
Note. The calculation of masking thresholds based
upon incoming analysis of audio is the basis of psychoacoustic compression
because the frequency with the highest local amplitude will tend
to mask (make inaudible) nearby frequencies below
the threshold.
(2)
Note. MPEG (Motion Picture Experts Group) sets
international standards such as MPEG 1, level 3 (commonly
called MP3) for psychoacoustic coding to achieve audio
compression of up to 10:1. Typical coders work
on a 16-bit PCM audio signal, which is the typical
CD quality standard.
(3)
Note. Only white noise in a bandwidth centered about
a tone and less than or equal to the critical bandwidth contributes
to the masking effect. Critical bands are generally considered
a set of filters or channels tuned to different center frequencies
having a bandwidth of less than a third of an octave.
(4)
Note. A plot of frequency versus pitch in mels is
similar in shape to the plot of frequency versus the position of
auditory-nerve patches on the basilar membrane.
This is evidence that human judgment of pitch is based upon the point
of excitation along the basilar membrane in the ear.
This subclass is indented under subclass 201. Subject matter wherein coding is performed using parallel
distributed processing elements constructed in hardware or simulated
in software.
This subclass is indented under subclass 201. Subject matter wherein the speech is
encoded using a specific mathematical function (e.g., Fourier, Walsh, cosine/sine
transform, etc.).
This subclass is indented under subclass 203. Subject matter wherein the function is orthogonal (transformations
as applied to vector, matrix, linear and polynomial
functions, for example).
This subclass is indented under subclass 206. Subject matter wherein the specific speech information
represents the predominant frequency of the speech.
This subclass is indented under subclass 207. Subject matter wherein the specific speech information
represents the presence (voiced) or absence (unvoiced) of predominant frequency components.
This subclass is indented under subclass 206. Subject matter wherein the specific speech information
represents the frequency values of any of several resonance bands
which determine the phonetic quality of a vowel sound.
This subclass is indented under subclass 201. Subject matter wherein the speech signal
is represented using time (e.g., time
measurements and energy measured over time).
This subclass is indented under subclass 211. Subject matter wherein the signal is sampled over time, and
the magnitude of each sample is quantized and converted into a digital
signal.
This subclass is indented under subclass 211. Subject matter wherein time measurements are used to determine
the presence (voiced) or absence (unvoiced) of predominant frequency components.
This subclass is indented under subclass 211. Subject matter wherein time measurements are used to determine
the presence or absence of speech (e.g., pauses
between words, etc.).
This subclass is indented under subclass 216. Subject matter wherein the relationships are between different speech samples taken from the same time
series.
This subclass is indented under subclass 201. Subject matter wherein the speech signal
is coded and corrected by the difference of the decoded coded signal
from the original speech signal.
This subclass is indented under subclass 221. Subject matter wherein the encoding maps a sequence of continuous
or discrete vectors into a digital sequence.
This subclass is indented under subclass 221. Subject matter wherein the encoding models speech using
representations including the primary frequency period or periods (e.g., pitchexcitation, multipulse excitation, etc.).
This subclass is indented under subclass 201. Subject matter wherein modifications of the speech signal
emphasize or deemphasize certain features (e.g., spectral
slope, average power, etc.).
This subclass is indented under subclass 226. Subject matter wherein decoding after transmission minimizes
the effects of noise in the transmission
path.
This subclass is indented under subclass 201. Subject matter wherein limited storage or transmission resources
are allocated by giving more resources to areas containing more
data and giving fewer resources to areas containing less data.
This subclass is indented under subclass 200. Subject matter wherein speech is
separated into discrete components which are distinguished from
one another.
This subclass is indented under subclass 231. Subject matter using parallel distributed processing elements
constructed in hardware or simulated in software.
This subclass is indented under subclass 231. Subject matter wherein the discrete components are modified
to emphasize or deemphasize certain features (e.g., spectral
slope, average power, etc.).
This subclass is indented under subclass 231. Subject matter wherein the distinguished discrete components
are converted into image output (e.g., text).
This subclass is indented under subclass 236. Subject matter wherein the specific function measures a correlation between discrete components (e.g., absolute
magnitude difference functions (AMDF), autocorrelation, cross-correlation, etc.).
This subclass is indented under subclass 236. Subject matter wherein the specific function uses probability
to determine the occurrence of a discrete component.
This subclass is indented under subclass 236. Subject matter wherein time components of the discrete components
are aligned with reference components (e.g., using
dynamic programming).
This subclass is indented under subclass 236. Subject matter wherein discrete components are distinguished
by traversing possible paths through a time series.
This subclass is indented under subclass 231. Subject matter including specific methods for registering
the discrete components to be used as references.
This subclass is indented under subclass 243. Subject matter wherein similar references are placed or
divided into groups (e.g., K-means algorithm, nearest
neighbor, etc.).
This subclass is indented under subclass 231. Subject matter wherein different voices are distinguished (e.g., speaker
identification or verification).
This subclass is indented under subclass 246. Subject matter including separating speech into sound
segments (e.g., utterances, words, phonemes, allophones, etc.).
This subclass is indented under subclass 251. Subject matter including models which describe the interconnections
between words or subportions of words.
This subclass is indented under subclass 255. Subject matter wherein the models include states which represent speech sound portions and transitions
which represent connections between speech sound
portions (e.g., hidden Markov
models, heuristic Markov models, etc.).
This subclass is indented under subclass 256. Subject matter wherein a Markov chain used in the recognition
process has un-observable (hidden) states.
(1)
Note. The subject matter in this subclass is substantially
the same in scope as ECLA (G10L 15/14M).
(2)
Note. The observation model itself is part of the
stochastic process (Markov Chain) with an underlying
stochastic process that is not directly observable, but
can be observed through a set of stochastic processes that produce
the sequence of observations.
(3)
Note. The HMM has different elements, including
the following – number of states, the number
of distinct observations per state, state transition probability
distribution, the observation symbol probability distribution, and
the initial state distribution.
(4)
Note. The manipulation of HMM s can be use in improving
the probability of observation sequences, optimizing state sequences, or
maximizing the probability of the state sequences.
(5)
Note. Subcategories to the types of HMM s include
finite state, discrete versus continuous, mixture
densities, autoregressive, null transition, tied states, and
state duration.
This subclass is indented under subclass 256.1. Subject matter wherein the models include a learning
process for recognizing speech data, e.g., the
construction of a library of models for the words in a vocabulary, including
the states.
(1)
Note. The subject matter in this subclass is substantially
the same in scope as ECLA (G10L 15/14M1).
This subclass is indented under subclass 256.2. Subject matter wherein intrinsic parameters of the HMM are
modified to overcome lack of training data, and to simplify
the model, e.g., state sharing, tying, and
deleted interpolation.
(1)
Note. The subject matter in this subclass is substantially
the same in scope as ECLA (G10L 15/14M1S).
(2)
Note. State sharing involves combining two or more
separately trained models, one of which is more reliably
trained than the other. The scenario in which this can
happen is the case when we use tied states which forces "different" states
to share an identical statistical characterization, effectively
reducing the number of parameters in the model.
(3)
Note. Parameter tying involves setting up an equivalence
relation between HMM parameters in different states. In this
manner the number of independent parameters in the model is reduced
and the parameter estimation becomes somewhat simpler and in some
cases more reliable. Parameter tying is used when the observation
density, for example, is known to be the same
in two or more states.
(4)
Note. Deleted interpolation is a parameter method
aimed to improve model reliability. The concept involves
combining two or more separately trained models, one of
which is more reliably trained than the other. The scenario
in which this can happen is the case when we use tied states which
forces "different" states to share an identical
statistical characterization, effectively reducing the
number of parameters in the model. The technique of deleted
interpolation has been successfully applied to a number of problems
in speech recognition, including the estimation of trigram
word probabilities for language models, and the estimation of
HMM output probabilities for trigram phone models.
This subclass is indented under subclass 256.1. Subject matter wherein the HMM includes a duration state
model for speech recognition, e.g., semi
HMM’s segmental models, and transition probabilities.
(1)
Note. The subject matter in this subclass is substantially
the same in scope as ECLA (G10L 15/14M2).
(2)
Note. A semi- Markov HMM is like an HMM
except each state can emit a sequence of observations.
(3)
Note. Within a state segment models introduce dependency
between frames via their common dependence on a trajectory. There
may be only a single trajectory or a continuous mixture of trajectories. The
probability distribution over the sequence of frames for a state, given
the duration and trajectory, is then typically modeled
as independent Gaussian distributions for each time step, centered
on the trajectory.
(4)
Note. Symbol emission probabilities are associated
to the states and transition probabilities to the connections between them.
This subclass is indented under subclass 256.1. Subject matter including a HMM structure wherein subgroups
of HMM types are used to perform speech recognition.
(1)
Note. The subject matter in this subclass is substantially
the same in scope as ECLA (G10L 15/14M3).
(2)
Note. Each subgroup can vary by type of model, model
size, and observation symbols.
This subclass is indented under subclass 256.1. Subject matter wherein the HMM contains probability density
function such that an emission probability is calculated for each
state within the model.
(1)
Note. The subject matter in this subclass is substantially
the same in scope as ECLA (G10L 15/14M4).
(2)
Note. For each state j, and
for each possible output, a probability that a particular
output symbol o is observed in
that state. This is represented by the function bj(o), which
gives the probability that o is
emitted in state j. This
is called the emission probability.
This subclass is indented under subclass 256.6. Subject matter wherein the HMM contains continuous probability
density observation models for the purpose of avoiding possible signal
degradation inherent with discrete representations of signals.
(1)
Note. The subject matter in this subclass is substantially
the same in scope as ECLA (G10L 15/14M4C).
This subclass is indented under subclass 256.6. Subject matter wherein the HMM contains discrete probability
density observation models which allows for the use of a discrete
probability density within each state of the model.
(1)
Note. The subject matter in this subclass is substantially
the same in scope as ECLA (G10L 15/14M4D).
(2)
Note. Discrete probability density is used when the
state of the model is discrete (e.g. representing
a letter of the alphabet). Vector quantization
is used to model its state.
This subclass is indented under subclass 200. Subject matter wherein component parts of a speech signal
are combined to produce a synthetic speech output.
This subclass is indented under subclass 258. Subject matter wherein synthetic speech output is
formed using parallel distributed processing elements constructed
in hardware or simulated in software.
This subclass is indented under subclass 258. Subject matter wherein the component parts are represented
by coefficients derived from a sequence of past speech samples.
This subclass is indented under subclass 258. Subject matter wherein the component parts are represented
by coefficients derived from relationships between time series speech samples.
This subclass is indented under subclass 258. Subject matter wherein the component parts are represented
by the period of the primary frequency of the speech signal (e.g., pitchexcitation, multi-pulse excitation, etc.).
This subclass is indented under subclass 258. Subject matter wherein the component parts are combined
using estimates of intermediate values (e.g., waveform
smoothing).
This subclass is indented under subclass 258. Subject matter wherein the component parts are combined
or linked together in a defined manner (e.g., Markov
models, trees, tries (tables representing
trees), graphs, etc.).
This subclass is indented under subclass 258. Subject matter wherein the component parts comprise time
based elements (e.g., words, phonemes, allophones, etc.).
This subclass is indented under subclass 258. Subject matter wherein the component parts comprise frequency
based elements (e.g., pitch variations, inflection, formants, etc.).
This subclass is indented under subclass 258. Subject matter wherein the component parts are restored
to speech using specific mathematical
functions (e.g., Fourier, Walsh, Hilbert, Z-transform, cosine/sine
transforms, etc.).
This subclass is indented under subclass 200. Subject matter intended or designed for a specified use
to which the speech signal processing is
being applied.
This subclass is indented under subclass 270. Subject matter wherein a system that employs speech recognition
or synthesis to control or to provide user feedback such that the
processing of speech data may occur at various levels within a computer
network.
(1)
Note. Various levels of processing would include
local or remote locations relative to the user in order to make
use of available resources. For example, a local
terminal might not have the necessary storage or processing power
but this can be overcome by accessing resources over a network.
Such resources may include the raw processing power necessary for
analysis and pattern matching as well as dictionaries having data
relevant to large vocabularies and multiple languages.
(2)
Note. Nominal recitations of speech or audio in
network applications are classified elsewhere.
Multiplex Communications,
subclasses 229 through 240for data flow congestion prevention or control, subclasses
260-269 for conferencing and subclass 351 for voice over
internet.
Data Processing: Database, Data
Mining, and File Management or Data Structures,
subclasses 770 , 966 through 974 and 999.010
for distributed databases searching and access.
Electrical Computers and Digital processing systems:
Multiple computer or Process Coordinating,
subclasses 227 through 229for network computer-to-computer
connections.
This subclass is indented under subclass 270. Subject matter for assisting handicapped people (e.g., blind
or speech impaired communication
and control).
This subclass is indented under subclass 270. Subject matter wherein speech is
edited using waveform portions or other representations of the sounds
to be modified.
This subclass is indented under the class definition. Subject matter where there is either an expansion or reduction
of the bandwidth required for transmission of a sound signal.
(1)
Note. This subclass and its indents provide for bandwidth
compression or expansion of audio signals other than speech signals.
Pulse or Digital Communications,
subclasses 240 through 241for bandwidth compression or expansion of a pulse
or digital signal, particularly subclasses 240.01-240.29 for digital television.
This subclass is indented under subclass 500. Subject matter combined with means to discard and replace
redundant information by a code indicating what has been discarded.
AUDIO SIGNAL TIME COMPRESSION OR EXPANSION (E.G., RUN LENGTH CODING):
This subclass is indented under the class definition. Subject matter where there is either an expansion or reduction
of the time required for transmission of a nonspeech sound
signal.
This subclass is indented under subclass 503. Subject matter combined with means to discard and replace
redundant information by a code indicating what has been discarded.
Coded Data Generation or Conversion,
subclass 55 for content reduction encoding, per se.
E-SUBCLASSES
The E-subclasses in U.S. Class 704 provide for methods and
devices for analyzing or synthesizing spoken language and for detecting,
recognizing, or modifying speech signal characteristics.
MISCELLANEOUS ANALYSIS OR DETECTION OF SPEECH CHARACTERISTICS
(EPO):
This main group provides for processes and apparatus for
analyzing or detecting speech characteristics not provided for elsewhere.
This subclass is substantially the same in scope as ECLA classification
G10L11/00.
This main group provides for processes and apparatus for
synthesizing speech. This subclass is substantially the same in
scope as ECLA classification G10L13/00.
This main group provides for processes, systems, and apparatus
for the recognition of speech, including training of speech recognition
systems, language recognition, speech classification and search,
speech-to-text systems, and evaluation or assessment of speech recognition
systems. This subclass is substantially the same in scope as ECLA
classification G10L15/00.
This main group provides for processes and apparatus for
recognizing special voice characteristics, systems using speaker
recognizers and details of speaker identification or verification
processes or apparatus. This subclass is substantially the same
in scope as ECLA classification G10L17/00.
SPEECH OR AUDIO SIGNAL ANALYSIS-SYNTHESIS TECHNIQUES FOR REDUNDANCY
REDUCTION, E.G., IN VOCODERS, ETC.; CODING OR DECODING OF SPEECH
OR AUDIO SIGNALS; COMPRESSION OR EXPANSION OF SPEECH OR AUDIO SIGNALS,
E.G., SOURCE-FILTER MODELS, PSYCHOACOUSTIC ANALYSIS, ETC. (EPO):
This main group provides for processes and apparatus for
the coding, decoding, compression or expansion of speech or audio
signals, including techniques for redundancy reduction, and psychoacoustic
analysis. This subclass is substantially the same in scope as ECLA
classification G10L19/00.
MODIFICATION OF AT LEAST ONE CHARACTERISTIC OF SPEECH WAVES (EPO):
This main group provides for processes and apparatus for
modifying at least one characteristic of a speech signal. This subclass
is substantially the same in scope as ECLA classification G10L21/00.
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