EP0815553A2 - Method of detecting a pause between two signal patterns on a time-variable measurement signal - Google Patents
Method of detecting a pause between two signal patterns on a time-variable measurement signalInfo
- Publication number
- EP0815553A2 EP0815553A2 EP96905679A EP96905679A EP0815553A2 EP 0815553 A2 EP0815553 A2 EP 0815553A2 EP 96905679 A EP96905679 A EP 96905679A EP 96905679 A EP96905679 A EP 96905679A EP 0815553 A2 EP0815553 A2 EP 0815553A2
- Authority
- EP
- European Patent Office
- Prior art keywords
- pause
- signal
- measurement signal
- pattern
- recognized
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000005259 measurement Methods 0.000 title claims abstract description 41
- 239000013598 vector Substances 0.000 claims abstract description 23
- 238000001514 detection method Methods 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 18
- 238000000605 extraction Methods 0.000 claims description 12
- 238000003909 pattern recognition Methods 0.000 claims description 12
- 230000003595 spectral effect Effects 0.000 claims description 7
- 230000011664 signaling Effects 0.000 claims description 4
- 108010076504 Protein Sorting Signals Proteins 0.000 claims description 3
- 230000006978 adaptation Effects 0.000 claims 1
- 238000004458 analytical method Methods 0.000 abstract description 17
- 230000008901 benefit Effects 0.000 abstract description 6
- 238000004891 communication Methods 0.000 abstract description 2
- 238000012549 training Methods 0.000 description 5
- 238000012567 pattern recognition method Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000005315 distribution function Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001427 coherent effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000029058 respiratory gaseous exchange Effects 0.000 description 1
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- 230000002123 temporal effect Effects 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000010626 work up procedure Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/78—Detection of presence or absence of voice signals
Definitions
- Pattern recognition processes can generally be reduced to a time-variant measurement signal, which is derived in a suitable manner from the patterns to be recognized.
- these measurement signals are not in pure form, but are often superimposed by stationary or unsteady interference signals.
- these storage components of the measurement signal can be caused, for example, by background noises, breathing noises, machine noises, or also by the recording medium and the transmission path.
- the measurement signal is never in pure form, it is particularly important to distinguish between the parts of the measurement signal which contain the pattern to be recognized and between other parts in which there is no pattern. For better recognition of the patterns, it is particularly important to know exactly when there are patterns in the measurement signal and when there are no patterns, i.e. signals not originating from the pattern are present as pause signals in the measurement signal.
- a pause detection is also important, for example, in order to achieve a reduction in the amount of data transmitted, for example in the case of voice communication channels and also in satellite transmission, for the general useful interference signal decision in signal processing, or for the end of an utterance in automatic speech recognition systems to find.
- a robust pause detector serves to improve the performance Ability of voice-controlled systems. This is particularly true for speech recognition systems, since the aim is to compare a spoken utterance as a pattern with an existing version.
- Rabmer [1] has described the problem of pause determination, especially in automatic speech recognition, in detail. He also specified an algorithm for pause detection. For pause detection, information is taken into account which is calculated directly from the sampled time signal (energy, zero crossing rate ETC.). This procedure is common to all known pause detectors [2].
- the object underlying the invention is an improved method for pause detection between patterns specify which are present in a measurement signal and which have been modeled using hidden Markov models.
- An advantage of the method according to the invention is that, for the first time, information which is obtained in different signal processing stages and which occurs in succession is used for pause detection.
- the pause information is obtained by comparing a special pause model with the feature vectors of the measurement signal in a comparison stage and returning it to the feature extraction stage of the pattern recognition, so that the pause state can be taken into account in a further time slice in the feature extraction stage in the measurement signal analysis .
- the method according to the invention takes advantage of the information that certain pattern groups belong together, for example in the case of words these are phoneme pattern groups, so it is ensured that a pause must follow at least after the pattern group.
- This information is then advantageously used in the feature extraction stage as the first processing stage of the method.
- the method according to the invention also ensures that there must have been a pause before a pattern sequence to be recognized arrives. This fact is also used in pattern recognition.
- the method according to the invention can advantageously be combined with known methods for pause detection, which properties of the measurement signal in the time domain and in Evaluate the spectral range. In this way, a higher detection rate in pattern recognition can be achieved.
- Speech patterns, writing patterns or signaling patterns can be analyzed particularly advantageously with the method according to the invention, since they occur in a variety of technical applications and can be modeled in a suitable manner.
- the method according to the invention can advantageously ensure that if there are no patterns detected, there must be a pause, in this way an increased detection rate is achieved in the pattern recognition, since pause information can thus be made available to the feature extraction level even more reliably.
- FIG. 1 shows a schematic example of a speech recognition system equipped with pause recognition.
- Figure 2 illustrates the pause detection process using various hidden Markov models.
- FIG. 1 shows, using an example, which is designed here as a speech recognition system, how the pause information is detected and passed on, ie returned, using the method according to the invention.
- the measurement signal here as a speech signal Spr initially reaches a feature extraction stage Merk which corresponds to the first signal processing stage in the method according to the invention.
- the spectral features of the speech signal or the measurement signal Spr are usually analyzed. These features, which are subsequently output by the feature extraction level, are designated here by m in FIG. 1.
- the spectral features m subsequently arrive, for example, as feature vectors in a classification level Klass, in which they are included the Hidden Markov HMM models.
- the method according to the invention comes into play, in that the feature vectors obtained from the measurement signals are compared in special hidden Markov models for individual phonemes or for pause states.
- special hidden Markov models for individual phonemes or for pause states.
- typical feature vectors for the background noise and for the useful signal are estimated. This makes it possible for a continuous pattern comparison to distinguish between useful and noise signals in every analysis interval. An even higher one
- the method according to the invention is based in particular on the fact that the signal states and the feature vectors do not change excessively from one time slice to the other time slice of the analysis interval.
- information that is obtained in the classification level Klass can be determined, for example, by comparing the hidden Markov models with a higher probability of pause than for a pattern to be recognized, to the feature extraction level as pause information Pa to get redirected. It is very likely that another time slice with a pause will follow the time slice in which the pause is detected. With this procedure, undesirable disturbances in the formation of the feature vectors present in the measurement signal can be suppressed with great certainty even with a low signal-to-noise ratio.
- the knowledge of the pause present in the recognition stage in a second time slice is advantageous by the method according to the invention a first signal processing stage is transmitted.
- This knowledge can be obtained, for example, from a speech signal via the acoustically phonetic modeling level (hidden Markov models), which has already been trained with a lot of the training data for speech recognition.
- the pause is trained as a model of a phoneme and thus includes the statistics of the training data. Modeling is more refined and therefore better when the phoneme context is taken into account, ie the knowledge of which phoneme follows another. If, for example, the pause decision of the acoustically phonetic modeling stage is linked to common criteria for estimating the pause, an improvement in the pause decision can be achieved.
- FIG. 2 shows the different Viterbi paths VI to V3 for different hidden Markov models.
- the relationship between pattern recognition and the presence of a pause between different patterns is shown here over time.
- the measurement signal which is, for example, a voice signal, a write signal or a signal that is emitted by signaling methods, is transformed into a feature vector space by means of a suitable signal transformation or a plurality of signal transformations.
- typical models for the background noise and also for the useful signal are estimated, for example, which are then to be used in the recognition method.
- the training can be implemented, for example, using the method of the hidden Markov models.
- the method for pause recognition can also be carried out equally with other pattern recognition methods, such as dynamic programming, or neural networks.
- hidden Markov models are used in the method according to the invention, the distribution functions of the feature vectors for each recognition unit can be estimated, for example.
- detection units in this context are meant in automatic speech recognition speech sounds (phonemes).
- the method according to the invention was implemented, for example, for automatic speech recognition, but it is conceivable that it can be used for any type of pattern recognition. It is only necessary to ensure that signal patterns can be provided and that there are pause states in which the interference signals can be determined in order to train the hidden Markov models for pause states.
- Some such examples of other pattern recognition methods are, for example, the patterns that occur when a document is signed in the form of pressure-dependent or time-dependent write signals, or signal sequences that are used in automatic message-based signaling methods.
- a continuous pattern comparison in every analysis interval or time slice can calculate the generation probability for each recognition unit.
- An easy solution is to evaluate these probabilities. If the probability of pause, that is to say for the hidden Markov model for pause or its equivalent, is the highest, the relevant analysis interval can be used to re-estimate the distribution functions or to filter out noise suppression.
- the method according to the invention becomes even more robust if the result of a pattern recognizer is taken into account as an additional source of knowledge. Assuming, for example, that the pattern recognizer is able to recognize every possible useful signal, the method according to the invention can take advantage of this and define all other analysis intervals, which are not classified as useful signals, as pauses. Such a time period is designated T p in FIG. If there are no requests for real-time processing If, for example, this is the case in simulations, the method according to the invention can hereby already be considered sufficient for pattern recognition. In practice, real-time criteria are to be used in the applications mentioned and the earliest possible assignment to the useful or noise signal must be made. The method must therefore be integrated, for example, into the recognition process itself.
- the recognition method is thus expanded in accordance with the invention in such a way that after each analysis step, for example, it is evaluated which of the patterns, for example words, composed of the recognition units is the most probable.
- the probability that it contains a signal pause is calculated over a larger analysis interval, for example.
- the analysis interval is dimensioned such that it is in any case longer than short pauses, for example plosive pauses, in the useful signal.
- This probability is then compared with that of the most probable pattern, whereby they are related to an equally long time interval. The result of this comparison can already be used as a decision.
- a signal pause is only recognized as the end of a word if, in addition to the criterion described above, the most likely word has always been the most likely word over a certain period of time. This time period is designated in FIG. 2 by T s ⁇ .
- the combination of these two criteria described provides a high level of reliability in the pause recognition, which is important for the reliable functioning of a speech recognizer.
- the basic idea is to use the knowledge sources available at different levels in signal processing stages in a pattern recognition system to detect a pause. These extend to, for example
- the spectral range e.g. the performance and the correlation measure including the logarithmic and / or feature range.
- the pause is detected by the method according to the invention by realizing a return from the recognition stage to the feature extraction stage.
- the information about the presence of a pause in the classifier Klass of the feature extraction level Merk is present in the different time slices.
- a dynamic pattern comparison takes place, in which an assignment to the pre-trained models is carried out on the basis of the feature vectors in an analysis window or a time slice.
- a global search strategy such as Realized by the Viterbi algorithm us finds the most probable sequence of pre-trained model states, which reflects the incoming sequence of feature vectors [6].
- the information about pause / non-pause can thus be tapped at the classifier Klass and fed to a pause detector in another stage.
- this is implemented, for example, by comparing a special hidden Markov model for pause with the incoming feature vectors in the classifier, if a higher probability of pause than for other patterns occurs, pause information is, for example passed on to the feature extraction level Merk and leads there to the decision that there is currently a pause. That means with this pause information a pause detector already present in the extraction stage can also be controlled in order to set pause.
- This pause decision can be probability-weighted, for example, and is based on a decision that takes into account other sources of knowledge within the inventive method.
- Such other sources of knowledge are, for example, statistics of the measurement signal and phoneme context from the Viterbi method. Because of the sequential structure of a recognizer, for example, when the information is returned to a pause detection stage for suppressing background noise, for example the delay by an analysis window must be taken into account. If the pause decision of the acoustically phonetic modeling stage in speech recognition is linked to common criteria for pause estimation, an improvement in the pause decision can be achieved. If, for example, one proceeds entirely from frame-by-frame detection of the breaks, a further source of knowledge in the detection system can be used for the break estimation.
- different coherent and also related patterns can be detected as a whole and conclusions can be drawn therefrom on pauses present in the measurement signal.
- a global pause detector can provide its information about the entire pattern to be recognized or the pattern sequence.
- Speech recognition would be such a pattern sequence, for example a word to be recognized. All areas except this pattern sequence can thus be recognized as a break, for example.
- This has the advantage that even current disturbances are included in the pause detection.
- the method according to the invention thus also works at very high interference levels, and is therefore more robust. In principle, a longer time delay must be taken into account until a decision is made.
- This global pause detection stage is therefore particularly useful in connection with intermediate signal storage. It is particularly suitable for processing the measurement signal and can in particular be used to detect the separation pauses between serve individual words or pattern sequences to be recognized.
- a pattern recognition and pause recognition system according to the invention can be described in the following stages.
- spectral range e.g. performance, correlation measure
- logarithmic and / or feature range e.g. logarithmic and / or feature range
- calculate_word_wk ()! calculates the! Probability, e.g. with hidden
- pause detector ()! sets pause to 1 if the probabilities
- the method according to the invention is implemented in a main program which is limited by main and end.
- This main program essentially contains a do-loop as a time loop.
- the signal_analysis procedure is used to transform the measurement signal into a feature area. For example, a special time slice of the measurement signal is analyzed and feature vectors are applied from this time slice.
- the created feature vectors are then analyzed in a subroutine calculate_word_wk. For example, the probability is calculated there for each reference word, for example using hidden Markov models and using Viterbi decoding. For example, the association probability that all previous feature vectors have been emitted is calculated.
- the probability for pause for the last P time steps is calculated in a further subroutine calculate_pause_wk.
- the association probability is calculated so that the last P-feature vectors were emitted by the model for pause.
- pause information is generated if the probability for pause is higher than for the best word, otherwise the pause information is not generated.
- the probability to be taken into account is normalized here to the same time period P.
- the procedure is aborted if pause was detected by pause detector and the best word has been stable at least for x periods (word_stable).
- the recognized pattern sequence, or a word in speech recognition is then output with the subroutine output.
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Computational Linguistics (AREA)
- Acoustics & Sound (AREA)
- Multimedia (AREA)
- Telephonic Communication Services (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Image Analysis (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
Description
Claims
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE19508711 | 1995-03-10 | ||
DE19508711A DE19508711A1 (en) | 1995-03-10 | 1995-03-10 | Method for recognizing a signal pause between two patterns which are present in a time-variant measurement signal |
PCT/DE1996/000379 WO1996028808A2 (en) | 1995-03-10 | 1996-03-04 | Method of detecting a pause between two signal patterns on a time-variable measurement signal |
Publications (2)
Publication Number | Publication Date |
---|---|
EP0815553A2 true EP0815553A2 (en) | 1998-01-07 |
EP0815553B1 EP0815553B1 (en) | 1999-06-02 |
Family
ID=7756346
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP96905679A Expired - Lifetime EP0815553B1 (en) | 1995-03-10 | 1996-03-04 | Method of detecting a pause between two signal patterns on a time-variable measurement signal |
Country Status (4)
Country | Link |
---|---|
US (1) | US5970452A (en) |
EP (1) | EP0815553B1 (en) |
DE (2) | DE19508711A1 (en) |
WO (1) | WO1996028808A2 (en) |
Families Citing this family (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
DE19705471C2 (en) * | 1997-02-13 | 1998-04-09 | Sican F & E Gmbh Sibet | Method and circuit arrangement for speech recognition and for voice control of devices |
DE19824355A1 (en) * | 1998-05-30 | 1999-12-02 | Philips Patentverwaltung | Apparatus for verifying time dependent user specific signals |
DE19824353A1 (en) * | 1998-05-30 | 1999-12-02 | Philips Patentverwaltung | Device for verifying signals |
DE19824354A1 (en) * | 1998-05-30 | 1999-12-02 | Philips Patentverwaltung | Device for verifying signals |
US6418411B1 (en) * | 1999-03-12 | 2002-07-09 | Texas Instruments Incorporated | Method and system for adaptive speech recognition in a noisy environment |
DE19939102C1 (en) * | 1999-08-18 | 2000-10-26 | Siemens Ag | Speech recognition method for dictating system or automatic telephone exchange |
DE10033104C2 (en) * | 2000-07-07 | 2003-02-27 | Siemens Ag | Methods for generating statistics of phone durations and methods for determining the duration of individual phones for speech synthesis |
US20020042709A1 (en) * | 2000-09-29 | 2002-04-11 | Rainer Klisch | Method and device for analyzing a spoken sequence of numbers |
JP4759827B2 (en) * | 2001-03-28 | 2011-08-31 | 日本電気株式会社 | Voice segmentation apparatus and method, and control program therefor |
WO2003054856A1 (en) * | 2001-12-21 | 2003-07-03 | Telefonaktiebolaget Lm Ericsson (Publ) | Method and device for voice recognition |
US20080249779A1 (en) * | 2003-06-30 | 2008-10-09 | Marcus Hennecke | Speech dialog system |
JP3909709B2 (en) * | 2004-03-09 | 2007-04-25 | インターナショナル・ビジネス・マシーンズ・コーポレーション | Noise removal apparatus, method, and program |
DE102004023824B4 (en) * | 2004-05-13 | 2006-07-13 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Apparatus and method for evaluating a quality class of an object to be tested |
US20070033041A1 (en) * | 2004-07-12 | 2007-02-08 | Norton Jeffrey W | Method of identifying a person based upon voice analysis |
US20090327036A1 (en) * | 2008-06-26 | 2009-12-31 | Bank Of America | Decision support systems using multi-scale customer and transaction clustering and visualization |
US8255218B1 (en) * | 2011-09-26 | 2012-08-28 | Google Inc. | Directing dictation into input fields |
US8543397B1 (en) | 2012-10-11 | 2013-09-24 | Google Inc. | Mobile device voice activation |
US9473094B2 (en) * | 2014-05-23 | 2016-10-18 | General Motors Llc | Automatically controlling the loudness of voice prompts |
US11283586B1 (en) | 2020-09-05 | 2022-03-22 | Francis Tiong | Method to estimate and compensate for clock rate difference in acoustic sensors |
Family Cites Families (12)
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US4481593A (en) * | 1981-10-05 | 1984-11-06 | Exxon Corporation | Continuous speech recognition |
US4587670A (en) * | 1982-10-15 | 1986-05-06 | At&T Bell Laboratories | Hidden Markov model speech recognition arrangement |
US4713777A (en) * | 1984-05-27 | 1987-12-15 | Exxon Research And Engineering Company | Speech recognition method having noise immunity |
US4811399A (en) * | 1984-12-31 | 1989-03-07 | Itt Defense Communications, A Division Of Itt Corporation | Apparatus and method for automatic speech recognition |
FR2581465B1 (en) * | 1985-05-03 | 1988-05-20 | Telephonie Ind Commerciale | METHOD AND DEVICE FOR CONTROLLING PROCESS BY SOUND PROCESS |
US5226091A (en) * | 1985-11-05 | 1993-07-06 | Howell David N L | Method and apparatus for capturing information in drawing or writing |
DE3784168T2 (en) * | 1987-09-23 | 1993-09-16 | Ibm | DIGITAL PACKAGE SWITCHING NETWORKS. |
JP2573352B2 (en) * | 1989-04-10 | 1997-01-22 | 富士通株式会社 | Voice detection device |
JPH04362698A (en) * | 1991-06-11 | 1992-12-15 | Canon Inc | Method and device for voice recognition |
US5293452A (en) * | 1991-07-01 | 1994-03-08 | Texas Instruments Incorporated | Voice log-in using spoken name input |
US5465317A (en) * | 1993-05-18 | 1995-11-07 | International Business Machines Corporation | Speech recognition system with improved rejection of words and sounds not in the system vocabulary |
JPH06332492A (en) * | 1993-05-19 | 1994-12-02 | Matsushita Electric Ind Co Ltd | Method and device for voice detection |
-
1995
- 1995-03-10 DE DE19508711A patent/DE19508711A1/en not_active Withdrawn
-
1996
- 1996-03-04 WO PCT/DE1996/000379 patent/WO1996028808A2/en active IP Right Grant
- 1996-03-04 EP EP96905679A patent/EP0815553B1/en not_active Expired - Lifetime
- 1996-03-04 US US08/894,977 patent/US5970452A/en not_active Expired - Lifetime
- 1996-03-04 DE DE59602095T patent/DE59602095D1/en not_active Expired - Lifetime
Non-Patent Citations (1)
Title |
---|
See references of WO9628808A2 * |
Also Published As
Publication number | Publication date |
---|---|
US5970452A (en) | 1999-10-19 |
EP0815553B1 (en) | 1999-06-02 |
WO1996028808A2 (en) | 1996-09-19 |
DE59602095D1 (en) | 1999-07-08 |
DE19508711A1 (en) | 1996-09-12 |
WO1996028808A3 (en) | 1996-10-24 |
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