Abstract: A learning data acquisition device or the like, capable of acquiring learning data by superimposing noise data on clean voice data at an appropriate SN ratio, is provided. The learning data acquisition device includes a voice recognition influence degree calculation unit and a learning data acquisition unit. The voice recognition influence degree calculation unit calculates an influence degree on voice recognition accuracy caused by a change of a signal-to-noise ratio, based on a result of voice recognition on the kth noise superimposed voice data and a result of voice recognition on the k?1th noise superimposed voice data, where K is an integer of 2 or larger, k=2, 3, . . .

Abstract: A voice/non-voice determination device robust with respect to an acoustic signal in a high-noise environment is provided. The voice/non-voice determination device includes an acoustic scene classification unit including a first model which receives input of an acoustic signal and outputs acoustic scene information which is information regarding a scene where the acoustic signal is collected, a speech enhancement unit including a second model which receives input of the acoustic signal and outputs speech enhancement information which is information regarding the acoustic signal after enhancement, and a voice/non-voice determination unit including a third model which receives input of the acoustic signal, the acoustic scene information and the speech enhancement information and outputs a voice/non-voice label which is information regarding a label of either a speech section or a non-speech section.

Abstract: Detection precision of a non-verbal sound is improved. An acoustic model storage unit 10A stores an acoustic model that is configured by a deep neural network with a bottleneck structure, and estimates a phoneme state from a sound feature value. A non-verbal sound model storage unit 10B stores a non-verbal sound model that estimates a posterior probability of a non-verbal sound likeliness from the sound feature value and a bottleneck feature value. A sound feature value extraction unit 11 extracts a sound feature value from an input sound signal. A bottleneck feature value estimation unit 12 inputs the sound feature value to the acoustic model and obtains an output of a bottleneck layer of the acoustic model as a bottleneck feature value. A non-verbal sound detection unit 13 inputs the sound feature value and the bottleneck feature value to the non-verbal sound model and obtains the posterior probability of the non-verbal sound likeliness output by the non-verbal sound model.

Abstract: A training speech data generating apparatus includes: a voice conversion unit that converts, using fourth noise data, which is noise data based on third noise data, and speech data, the speech data so as to make the speech data clearly audible under a noise environment corresponding to the fourth noise data; and a noise superimposition unit that obtains training speech data by superimposing the third noise data and the converted speech data.

Abstract: Execution speed of a thread is dynamically restored while reducing influence on processing accuracy of the whole system. A thread synchronization device (10) achieves synchronization among N threads (1-1, . . . , 1-N) to perform parallel processing by dividing time-series data into a plurality of pieces of data. Environment variables which achieve a trade-off between processing accuracy and execution speed are individually set at the threads (1-1, . . . , 1-N). An execution speed calculation unit (2) calculates execution speed of each of the threads. An environment variable update unit (3) updates the environment variables in accordance with the execution speed of the threads.

Abstract: Recognition results are acquired with high responsiveness without being affected by a network communication state. A speech recognition control device (1) acquires recognition results from a speech recognition device (2) with which it communicates through a network (3) and a speech recognition unit (13). A communication state measuring unit (11) measures a communication state of the network (3). A speech recognition requesting unit (12) transmits a request for a speech recognition process to each of the speech recognition device (2) and the speech recognition unit (13) with a timeout time set in accordance with an immediately prior communication state of the network (3). A recognition result output unit (14) outputs a recognition result based on a recognition result received from one or recognition results received from both of the speech recognition device (2) and the speech recognition unit (13).

Abstract: A voice/non-voice determination device robust with respect to an acoustic signal in a high-noise environment is provided. The voice/non-voice determination device includes an acoustic scene classification unit including a first model which receives input of an acoustic signal and outputs acoustic scene information which is information regarding a scene where the acoustic signal is collected, a speech enhancement unit including a second model which receives input of the acoustic signal and outputs speech enhancement information which is information regarding the acoustic signal after enhancement, and a voice/non-voice determination unit including a third model which receives input of the acoustic signal, the acoustic scene information and the speech enhancement information and outputs a voice/non-voice label which is information regarding a label of either a speech section or a non-speech section.

Abstract: An utterance section detection device which is capable of detecting an utterance section with high accuracy on the basis of whether or not an end of a speech section is an end of utterance.

Abstract: Provided is a technology of learning an acoustic model with a certain degree of accuracy of sound recognition within a short calculation period.

Abstract: A learning data acquisition device or the like, capable of acquiring learning data by superimposing noise data on clean voice data at an appropriate SN ratio, is provided. The learning data acquisition device includes a voice recognition influence degree calculation unit and a learning data acquisition unit. The voice recognition influence degree calculation unit calculates an influence degree on voice recognition accuracy caused by a change of a signal-to-noise ratio, based on a result of voice recognition on the kth noise superimposed voice data and a result of voice recognition on the k?1th noise superimposed voice data, where K is an integer of 2 or larger, k=2, 3, . . .

Abstract: Detection precision of a non-verbal sound is improved. An acoustic model storage unit 10A stores an acoustic model that is configured by a deep neural network with a bottleneck structure, and estimates a phoneme state from a sound feature value. A non-verbal sound model storage unit 10B stores a non-verbal sound model that estimates a posterior probability of a non-verbal sound likeliness from the sound feature value and a bottleneck feature value. A sound feature value extraction unit 11 extracts a sound feature value from an input sound signal. A bottleneck feature value estimation unit 12 inputs the sound feature value to the acoustic model and obtains an output of a bottleneck layer of the acoustic model as a bottleneck feature value. A non-verbal sound detection unit 13 inputs the sound feature value and the bottleneck feature value to the non-verbal sound model and obtains the posterior probability of the non-verbal sound likeliness output by the non-verbal sound model.

Abstract: A training speech data generating apparatus includes: a voice conversion unit that converts, using fourth noise data, which is noise data based on third noise data, and speech data, the speech data so as to make the speech data clearly audible under a noise environment corresponding to the fourth noise data; and a noise superimposition unit that obtains training speech data by superimposing the third noise data and the converted speech data.

Abstract: A piezoelectric body (piezoelectric layer 14) is configured to include a first piezoelectric layer 14a, a second piezoelectric layer 14b, and a third piezoelectric layer 14c. A piezoelectric constant of each of the first and third piezoelectric layer 14a and 14c is set to be smaller than that of the second piezoelectric layer 14b. Thus, it is possible to reduce an internal stress generated at an interface between first and second electrode layers 13 and 15 corresponding to the first and third piezoelectric layer 14a and 14c, and generation of a crack caused by high voltage application or driving performed for a long time is expected to be greatly reduced.

Abstract: A thin film magnetic head which comprises a first magnetic block made of nonconductive magnetic material having an indented portion in a part thereof, a lead formed in the indented portion having a part thereof being covered with a first insulating material, a coil layer formed on the first magnetic block and connected to the lead at one end thereof and having a part which is arranged to overlap with the lead by way of the first insulating material, a second insulating material formed on an end portion of the first magnetic block at the side thereof facing a storage medium, and a second magnetic block connected onto the coil layer and the second insulating material.