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Linguistic knowledge for Speech recognition

Linguistic knowledge for Speech recognition. By : Ahmed Aly 06/05/2013. Project description. The main goal of this project is to study the effect of using linguistics knowledge on the task of speech recognition. I am studying the usage of such knowledge in the following contexts :

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Linguistic knowledge for Speech recognition

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  1. Linguistic knowledge for Speech recognition By : Ahmed Aly 06/05/2013

  2. Project description • The main goal of this project is to study the effect of using linguistics knowledge on the task of speech recognition. • I am studying the usage of such knowledge in the following contexts : • Using higher level linguistics knowledge for speech recognition error correction • Using prosody models for conversational speech recognition • The effect of using syntactic and semantic information on the performance of speech recognition error detection • The effect of using prosody features in spoken speech subjectivity analysis

  3. Work done so far … • Studying the theoretical part of the automatic speech recognizer module • Literature review of the usage of linguistics knowledge in the speech recognition task • Literature review of the usage of prosody in spoken speech subjectivity analysis

  4. Main findings so far … • In the context of error correction It has been shown that the usage of syllable-based models has shown s a superior performance in domain-specific IR applications in comparison to word-based models • In this case a relatively smaller training set is needed. And it could handle intra-word transformation and syllable-to-syllable transformation • The usage of semantic knowledge has proved also to be successful in the task of error correction. • An example of this semantic knowledge is lexico-semantic pattern (LSP) which is a structure where linguistic entries and semantic types are used in combination to abstract certain sequences of the words in a text

  5. Main findings so far … (continued) • In the context of prosody experiments in acoustic model clustering show that representing syllable position and stress as conditioning factors lead to gains in recognition performance and reduced system complexity • Other experiments with pronunciation modeling show gains in surface-form phone prediction due to directly conditioning on acoustic-prosodic features • For subjectivity analysis, It has been shown that the usage of prosody features is not very usefull as Ngram characters and words features outperformed prosody features

  6. Q&A

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