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Timestamp: 2014-09-23 11:37:13
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Patent US7418385 - Voice detection device - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsThis voice detection device is composed of a myoelectric signal acquisition part for acquiring, from a plurality of regions, myoelectric signals generated at the time of a vocalization operation, a parameter calculation part for calculating, as parameters, the fluctuations of the acquired myoelectric...http://www.google.com/patents/US7418385?utm_source=gb-gplus-sharePatent US7418385 - Voice detection deviceAdvanced Patent SearchPublication numberUS7418385 B2Publication typeGrantApplication numberUS 10/869,945Publication dateAug 26, 2008Filing dateJun 18, 2004Priority dateJun 20, 2003Fee statusPaidAlso published asCN1272766C, CN1573927A, DE602004011292D1, DE602004011292T2, EP1489597A2, EP1489597A3, EP1489597A8, EP1489597B1, US20050027529Publication number10869945, 869945, US 7418385 B2, US 7418385B2, US-B2-7418385, US7418385 B2, US7418385B2InventorsHiroyuki Manabe, Kouki Hayashi, Takashi Ninjouji, Toshiaki Sugimura, Akira HiraiwaOriginal AssigneeNtt Docomo, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (15), Non-Patent Citations (6), Classifications (16), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetVoice detection deviceUS 7418385 B2Abstract This voice detection device is composed of a myoelectric signal acquisition part for acquiring, from a plurality of regions, myoelectric signals generated at the time of a vocalization operation, a parameter calculation part for calculating, as parameters, the fluctuations of the acquired myoelectric signals relative to a predetermined value in every channel corresponding to one of the plurality of regions, a vowel vocalization recognition part for specifying the vowel vocalization operation timing at the time of the vocalization operation, based on the fluctuations of the calculated parameters, and a vowel specification part for specifying a vowel corresponding to the vocalization operation, based on the fluctuation condition of the parameters before and after the specified vocalization operation timing in every channel.
[Non-Patent Document 1] Noboru Sugie et al., �A speech Employing a Speech Synthesizer Vowel Discrimination from Perioral Muscles Activities and Vowel Production, � IEE transactions on Biomedical Engineering, Vo. 32, No. 7
[Non-Patent Document 2] Manabe, Hiraiwa and Sugimura, �non-phonation voice recognition using myoelectric signals,� Interaction 2002 Collected Papers, 2002, p. 181-182.
SUMMARY OF THE INVENTION In a technique to perform a voice recognition based on myoelectric signals as described above, as with the voice recognition using usual speech signals, learning data to learn a recognition engine becomes necessary, and a vast amount of data will be required to enhance an accuracy of recognition.
DESCRIPTION OF THE PREFERRED EMBODIMENTS The idea of the present invention will be easily understood with reference to the accompanying drawings prepared by way of example only and in connection with the detailed description hereinbelow. Subsequently, an embodiment of this invention will be described with reference to the accompanying drawings. If possible, same parts are designated with same reference numerals and overlapping descriptions are omitted.
1 2 ⁢ T ⁢ ∫ - T T ⁢ ⅇ 2 ⁡ ( t + τ ) ⁢ ⅆ τ ( 1 ) Note that as another parameter associated with an activity amount of muscles, there is an average rectification value (ARV) of myoelectric signals, defined by the equation (2)
∫ - ∞ ∞ ⁢ h ⁡ ( τ ) ⁢  e ⁡ ( t + τ )  ⁢ ⅆ τ ( 2 ) Here, for the equation (2) the following equation (3) is defined.
∫ - ∞ ∞ ⁢ h ⁡ ( τ ) ⁢ ⅆ τ = 1 ( 3 ) Further, as another parameter associated with an activity amount of muscles, there is an integral average of myoelectric signals (IEMG (integral myoelectrogram)), defined by the equation (4).
∫ T T + Δ ⁢ ⁢ τ ⁢  e ⁡ ( t + τ )  ⁢ ⅆ τ ( 4 ) One may use any of the root mean square (RMS), the average rectification value (ARV), the integral electromyogram (IEMG) and a combination thereof. Further, one may use another parameter such as a frequency spectrum or the like. In this embodiment the root mean square is used.
An example of a parameter calculated by the parameter calculation part 102 is shown in FIG. 5. FIG. 5 illustrates the root mean square values (RMS values) in a time series in the case of the vocalization of �/a/� from a rest condition and the subsequent vocalization of �/i/�. Note that data 50 indicates a parameter (second parameter) at the child time window with a predetermined time being set at 50 ms, and data 51 indicates a parameter (second parameter) at the time window with a predetermined time being set at 200 ms. Note that in an example shown in FIG. 5 a cycle of the time window is set at 100 ms and a cycle of the child time window is set at 25 ms, so that a cycle of the change in the parameters are set at 100 ms and 25 ms, respectively. The method of processing data shown n FIG. 5 is described later. Parameter calculation part 102 outputs the parameters thus calculated to the vowel production recognition part 103.
P ′ ⁡ ( n ) =  p ⁡ ( t n + 1 ) - ∑ i = n - j n ⁢ w i ⁢ p ⁡ ( t i )  ( 6 ) Further, as a amount of temporal change P′ (n) one may employ, as defined by the equation (7), the quotient of the absolute value of the difference between the sum of the weighted parameters up to immediately before a predetermined time and the proximate parameter divided by the value of the proximate parameter. In this case, the degree of a change in the value of the parameter is expressed by a ratio.
P ′ ⁡ ( n ) =  p ⁡ ( t n + 1 ) - ∑ i = n - j n ⁢ w i ⁢ p ⁡ ( t i )  p ⁡ ( t n + 1 ) ( 7 ) As an amount of temporal change P′ (n), which would be satisfactory if it could specify the degree of change of the proximate calculated parameter from the parameter calculated in the past, one may employ, instead of the absolute value as with the equations (5)-(7), the value of the difference itself.
Vowel production recognition part 103 specifies a vowel component vocalized proximately (S02). In this embodiment, the vowel component vocalized immediately before has already been recognized, and the vowel production recognition part 103 specifies this already recognized vowel component as the one vocalized proximately. As the procedure to recognize the vowel component vocalized immediately before, one can use a known voice recognition procedure. Further, for example, one may make it a rule to ask a user to initially pronounce �ah,� and have the vocalized vowel component recognized. Vowel production recognition part 103 references, based on this specified vowel component, the information stored in the threshold information storage part 200 and acquires a corresponding threshold (step S03). As shown in FIG. 7, in the threshold information storage part 200, �proximate vowel components� and �thresholds� are stored, associated with each other. �Thresholds� are stored for each channel and, for example, if a �proximate vowel component� is �/a/�, then a �threshold� is 0.5 for �channel 1�, 0.2 for �channel 2� and 0.2 for �channel 3�. The function of this �threshold� is similar to that of a �change characteristic� as described later, and the �threshold� corresponds to a short time window (child time window) and the �change characteristic� corresponds to a long time window.
Vowel specification part 104 specifies a vowel component being vocalized proximately (step S12). Vowel specification part 104 recognizes a vowel component upon a comparison of information stored in the myoelectric information storage part 201 and the change characteristic of each channel, based on this specified vowel component (step S13). An example of information stored in the myoelectric information storage part 201 is shown in FIGS. 9A-9C. FIG. 9A shows the correspondence between an immediately preceding vowel component at orbicularis oris and the change characteristic, FIG. 9B shows the correspondence between an immediately preceding vowel component at zygomaticus major and the change characteristic, and FIG. 9C shows the correspondence between an immediately preceding vowel component at digastricus and the change characteristic. For example, if the change characteristic for each channel is �equal� at orbicularis oris, �increase� at zygomaticus major, and �decrease� at digastricus, it recognizes, based on information shown in FIGS. 9A-9C, the vocalized vowel as �/i/�. Note that in FIGS. 9A-9C �eql � means �equal�, �dec� means �decrease�, �inc� means �increase� and �sig inc� means �significant increase�.
Vowel specification part 904 is a part for specifying a vowel corresponding to a vocalization operation based on the monitoring result of the fluctuation monitoring part 903 and the parameters. Vowel specification part 904 specifies a proximate vowel component (step S26). In this embodiment, a vowel component being vocalized immediately before has already been recognized, and the vowel production recognition part 103 specifies this already recognized vowel component as the one being vocalized proximately. As the procedure to recognize a vowel component being vocalized immediately before, it is possible to use a known voice recognition procedure. Further, for example, one may make it a rule to ask a user to initially pronounce �ah� and have the vocalized vowel component recognized. Vowel specification part 904 specifies a vowel component based on this specified proximate vowel component and the information stored in the myoelectric signal storage part 910 (step S27). FIGS. 11A-1C show an example of the information stored in the myoelectric information storage part 910. FIG. 11A shows the correspondence between the immediately preceding vowel component at orbicularis oris and the change characteristic, FIG. 11B shows the correspondence between the immediately preceding vowel component at zygomaticus major and the change characteristic, and FIG. 11C shows the correspondence between the immediately preceding vowel component at digastricus and the change characteristic. For example, if the immediately preceding vowel component is �/a/�, and the change characteristic of each channel is �equal� at orbicularis oris, �increase� at zygomaticus major and �decrease� at digastricus, then it recognizes the vocalized vowel to be �/i/�, based on the information shown in FIG. 11C. The point different from the subject matter described with reference to FIG. 9A-9C is that, in order to admit the case where the immediately preceding vowel component and the recognition result are the same, the relevant part is now deemed to be �equal�. Here it is assumed that a �significant increase� means the case of an increase not less than 200% from the level of the preceding condition, an �increase� means the case of an increase not less than 50% to below 200% from the level of the preceding condition, �equal� means a change amount less than �50% from the level of the preceding condition, and a �decrease� means a decrease not less than 50% from the level of the preceding condition.
Patent CitationsCited PatentFiling datePublication dateApplicantTitleUS4343969 *Aug 1, 1980Aug 10, 1982Trans-Data AssociatesApparatus and method for articulatory speech recognitionUS4401851 *Mar 2, 1981Aug 30, 1983Tokyo Shibaura Denki Kabushiki KaishaVoice recognition apparatusUS4769844 *Apr 3, 1987Sep 6, 1988Ricoh Company, Ltd.Voice recognition system having a check scheme for registration of reference dataUS4937870 *Nov 14, 1988Jun 26, 1990American Telephone And Telegraph CompanySpeech recognition arrangementUS5642470 *Sep 27, 1994Jun 24, 1997Fujitsu LimitedSinging voice synthesizing device for synthesizing natural chorus voices by modulating synthesized voice with fluctuation and emphasisUS5794203 *Mar 22, 1994Aug 11, 1998Kehoe; Thomas DavidBiofeedback system for speech disordersUS5907826 *Oct 28, 1997May 25, 1999Nec CorporationSpeaker-independent speech recognition using vowel/consonant segmentation based on pitch intensity valuesUS5966690 *Jun 7, 1996Oct 12, 1999Sony CorporationSpeech recognition and synthesis systems which distinguish speech phonemes from noiseUS6343269 *May 27, 1999Jan 29, 2002Fuji Xerox Co., Ltd.Speech detection apparatus in which standard pattern is adopted in accordance with speech modeUS6795807 *Aug 17, 2000Sep 21, 2004David R. BaraffMethod and means for creating prosody in speech regeneration for laryngectomeesUS6970819 *Oct 27, 2000Nov 29, 2005Oki Electric Industry Co., Ltd.Speech synthesis deviceEP1341159A1Feb 27, 2003Sep 3, 2003NTT DoCoMo, Inc.Speech information recognition device and speech information recognition method based on myoelectrical signal analysisJPH0643897A Title not availableJPH07181888A Title not availableJPS52112205A Title not available* Cited by examinerNon-Patent CitationsReference1A. D. C. Chan, et al., "Hidden Markov Model Classification of Myoelectric Signals in Speech", 2001 Proceedings of the 23<SUP>rd </SUP>Annual EMBS International Conference, XP-010594764, vol. 1 of 4, conf. 23, Oct. 25, 2001, pp. 1727-1730.2Hiroyuki Manabe, et al., "Non-phonation voice recognition using myoelectric signals", Interaction 2002 Collected Papers, 2002, pp. 181-182.3Hiroyuki Manabe, et al., "Unvoiced Speech Recognition using EMG -Mime Speech Recognition-", CHI 2003: New Horizons, Conference on Human Factors in Computing Systems, XP-002375973, Apr. 5, 2003, pp. 794-795.4 *Hudgins et al., ("A New Strategy for Multifunction Myoelectric Control," IEEE Transactions on Biomedical Engineering, vol. 40, No. 1, Hanuary 1993, pp. 82-94).5Noboru Sugie, et al., A Speech Prosthesis Employing a Speech Synthesizer-Vowel Discrimination from Perioral Muscle Activities and Vowel Production, IEEE Transactions on Biomedical Engineering, vol. BME-32, No. 7, Jul. 1985, pp. 485-490.6 *Watson et al., ("Statistical Analyses of Electromyographic Activity in Spasmodic Dysphonic and Normal Control Subjects," Journal of Voice, vol. 9, No. 1 1995, pp. 3-15).* Cited by examinerClassifications U.S. Classification704/254, 704/E11.007, 704/271, 704/270, 704/E15.041, 704/243, 704/249International ClassificationG10L15/24, G10L11/06, G10L15/04, G10L11/02, G10L15/28Cooperative ClassificationG10L15/24, G10L25/93European ClassificationG10L25/93, G10L15/24Legal EventsDateCodeEventDescriptionJan 25, 2012FPAYFee paymentYear of fee payment: 4Oct 18, 2004ASAssignmentOwner name: NTT DOCOMO, INC., JAPANFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MANABE, HIROYUKI;HIRAIWA, YUMIKO, LEGAL HEIR OF AKIRA HIRAIWA (DECEASED);HAYASHI, KOUKI;AND OTHERS;REEL/FRAME:015893/0045Effective date: 20040629RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services©2012 Google