Kabushiki Kaisha Toshiba

Pei Ding

Jie Hao

Kun Yong

Huifeng Zhu

Jian Luan

Liqiang He

Yan Deng

Lei He

According to one embodiment, an apparatus for training a neural network acoustic model includes a calculating unit, a clustering unit, and a sharing unit. The calculating unit calculates, based on training data including a training speech and a labeled phoneme state, scores of phoneme states different from the labeled phoneme state. The clustering unit clusters a phoneme state whose score is larger than a predetermined threshold and the labeled phoneme state. The sharing unit shares probability of the labeled phoneme state by the clustered phoneme states. The training unit trains the neural network acoustic model based on the training speech and the clustered phoneme states.

Apparatus and method for training a neural network acoustic model, and speech recognition apparatus and method

According to one embodiment, an apparatus for optimizing a speech recognition result comprising: a receiving unit configured to receive a speech recognition result; a calculating unit configured to calculate a pronunciation similarity between a segment of the speech recognition result and a key word in a key word list; and a replacing unit configured to replace the segment with the key word in a case that the pronunciation similarity is higher than a first threshold.

Method and apparatus for optimizing a speech recognition result

According to one embodiment, an apparatus for applying pitch features in automatic speech recognition is provided. The apparatus includes a distribution evaluation module, normalization module, and random value adjusting module. The distribution evaluation module evaluates the global distribution of pitch features of voiced frames in speech signals, and the global distribution of random values for unvoiced frames in speech signals. The normalization module normalizes the global distribution of random values for unvoiced frames based on the global distribution of pitch features of voiced frames. The random value adjusting module adjusts random values for unvoiced frames based on the normalized global distribution, so that the adjusted random values can be assigned to unvoiced frames in speech signals as pitch features of the unvoiced frames.

Apparatus and method for applying pitch features in automatic speech recognition

The present invention provides a method and apparatus for enrollment and verification of speaker authentication. The method for enrollment of speaker authentication, comprising: extracting an acoustic feature vector sequence from an enrollment utterance of a speaker; and generating a speaker template using the acoustic feature vector sequence; wherein said step of extracting an acoustic feature vector sequence comprises: generating a filter-bank for the enrollment utterance of the speaker for filtering locations and energies of formants in the spectrum of the enrollment utterance based on the enrollment utterance; filtering the spectrum of the enrollment utterance by the generated filter-bank; and generating the acoustic feature vector sequence from the filtered enrollment utterance.

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Beijing, China

Pei Ding