Intel Corporation

Sony Corporation

Chinese Academy of Sciences

Yonghong Yan

Baosheng Yuan

Zhiwei Lin

Yibao Zhao

Qingwei Zhao

Weiguo Wu

Kun Liu

Ying Jia

Xiaoxing Liu

Li Lu

Qing Guo

Hongbin Suo

Quingwei Zhao

This invention relates to retrieval for multimedia content, and provides a program endpoint time detection apparatus for detecting an endpoint time of a program by performing processing on audio signals of said program, comprising an audio classification unit for classifying said audio signals into a speech signal portion and a non-speech signal portion; a keyword retrieval unit for retrieving, as a candidate endpoint keyword, an endpoint keyword indicating start or end of the program from said speech signal portion; a content analysis unit for performing content analysis on context of the candidate endpoint keyword retrieved by the keyword retrieval unit to determine whether the candidate endpoint keyword is a valid endpoint keyword; and a program endpoint time determination unit for performing statistics analysis based on the retrieval result of said keyword retrieval unit and the determination result of said content analysis unit, and determining the endpoint time of the program. In addition, this invention also provides a program information retrieval system. With present invention, program information regarding a program attended by user can be rapidly obtained.

Program endpoint time detection apparatus and method, and program information retrieval system

A method and system that expands a word graph to a phone graph. An unknown speech signal is received. A word graph is generated based on an application task or based on information extracted from the unknown speech signal. The word graph is expanded into a phone graph. The unknown speech signal is recognized using the phone graph. The phone graph can be based on a cross-word acoustical model to improve continuous speech recognition. By expanding a word graph into a phone graph, the phone graph can consume less memory than a word graph and can reduce greatly the computation cost in the decoding process than that of the word graph thus improving system performance. Furthermore, continuous speech recognition error rate can be reduced by using the phone graph, which provides a more accurate graph for continuous speech recognition.

Method and system for expanding a word graph to a phone graph based on a cross-word acoustical model to improve continuous speech recognition

According to one aspect of the invention, a method is provided in which a set of multiple mixture monophone models is created and trained to generate a set of multiple mixture context dependent models. A set of single mixture triphone models is created and trained to generate a set of context dependent models. Corresponding states of the triphone models are clustered to obtain a set of tied states based on a decision tree clustering process. Parameters of the context dependent models are estimated using a data dependent maximum a posteriori (MAP) adaptation method in which parameters of the tied states of the context dependent models are derived by adapting corresponding parameters of the context independent models using the training data associated with the respective tied states.

Method, apparatus, and system for building context dependent models for a large vocabulary continuous speech recognition (LVCSR) system

In some embodiments, the invention includes calculating estimated weights for identified errors in recognition of utterances. Sections of the utterances are marked as being misrecognized and the corresponding estimated weights are associated with these sections of the utterances. The weighted sections of the utterances are used to convert a speaker independent model to a speaker dependent model.

Speaker adaptation using weighted feedback

A method and system are provided in which a decision tree-based model ('general model') is scaled down ('trim-down') for a given task. The trim-down model can be adapted for the given task using task specific data. The general model can be based on a hidden markov model (HMM). By allowing a decision tree-based acoustic model (&#x201c;general model&#x201d;) to be scaled according to the vocabulary of the given task, the general model can be configured dynamically into a trim-down model, which can be used to improve speech recognition performance and reduce system resource utilization. Furthermore, the trim-down model can be adapted/adjusted according to task specific data, e.g., task vocabulary, model size, or other like task specific data.

Method and system to scale down a decision tree-based hidden markov model (HMM) for speech recognition

A method and system providing a statistical representation from rule-based grammar specifications. The language model is generated by obtaining a statistical representation of a rule-based language model and combining it with a statistical representation of a statistical language model for use as a final language model. The language model may be enhanced by applying smoothing and/or adapting for use as the final language model.

Method and system for building a domain specific statistical language model from rule based grammar specifications

A method and system for providing a class-based statistical language model representation from rule-based knowledge is disclosed. The class-based language model is generated from a statistical representation of a class-based rule net. A class-based rule net is generated using the domain-related rules with words replaced with their corresponding class-tags that are manually defined. The class-based statistical representation from the class-based rule net is combined with a class-based statistical representation from a statistical language model to generate a language model. The language model is enhanced by smoothing/adapting with general-purpose and/or domain-related corpus for use as the final language model. A two-pass search algorithm is applied for speech decoding.

Method and system for using rule-based knowledge to build a class-based domain specific statistical language model

According to one aspect of the invention, a method is provided in which knowledge about tone characteristics of a tonal syllabic language is used to model speech at various levels in a bottom-up speech recognition structure. The various levels in the bottom-up recognition structure include the acoustic level, the phonetic level, the work level, and the sentence level. At the acoustic level, pitch is treated as a continuous acoustic variable and pitch information extracted from the speech signal is included as feature component of feature vectors. At the phonetic level, main vowels having the same phonetic structure but different tones are defined and modeled as different phonemes. At the word level, as set of tone changes rules is used to build transcription for training data and pronunciation lattice for decoding. At sentence level, a set of sentence ending words with light tone are also added to the system vocabulary.

Method, apparatus, and system for bottom-up tone integration to Chinese continuous speech recognition system

A search method based on a single triphone tree for large vocabulary continuous speech recognizer is disclosed in which speech signal are received. Tokens are propagated in a phonetic tree to integrate a language model to recognize the received speech signals. By propagating tokens, which are preserved in tree nodes and record the path history, a single triphone tree can be used in a one pass searching process thereby reducing speech recognition processing time and system resource use.

Search method based on single triphone tree for large vocabulary continuous speech recognizer

In some embodiments, the invention involves receiving phonetic samples and assembling a two-level phonetic decision tree structure using the phonetic samples. The decision tree has multiple leaf node levels each having at least one state, wherein a least one node in a second level is assigned a Gaussian of a node in the first level, but the at least one node in the second level has a weight computed for it.

Acoustic modeling using a two-level decision tree in a speech recognition system

In some embodiments, the invention involves a method including segmenting an utterance into at least a first segment and a second segment, wherein a boundary between the first and second segments corresponds to a break in the utterance. The method further includes selecting potential hypothetical paths of potential words in the first and second segments that cross the boundary. The method also includes applying a language model to the potential hypothetical paths crossing to determine whether to merge the first and second segments and to apply decoding to the merged segments.

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Beijing, China

Yonghong Yan