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A speech recognition architecture combining topic detection and topic-dependent language modeling is proposed. In this ar- chitecture, a hierarchical back-off mechanism is introduced to improve system robustness. Detailed topic models are applied when topic detection is confident, and wider models that cover multiple topics are applied in cases of...
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... topic hierarchy is automatically constructed by clustering together those topics likely to be con- fused during topic detection. An example topic hierarchy based Figure 2. The top node corresponds to a topic-independent G-LM that gives complete coverage of all topics, the bottom layer corresponds to the most detailed, individual topic models, and the intermediate nodes corresponds to models that cover multiple topics. ...
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Citations
... There are dialogue systems designed to recognize sentences by considering a specific language model associated with each system prompt decided by the dialogue manager (Lane et al., 2003; Mori et al., 2003 ), which is the case, e.g., of SAPLEN. These language models, called prompt-dependent language models in this paper, aim to provide high speech recognition rates and are useful if the interaction is clearly constrained by the system; however, they are not adequate if the user does not follow the system indications and utters sentences not permitted by these language models. ...
This paper presents a new technique to enhance the performance of the input interface of spoken dialogue systems based on a procedure that combines during speech recognition the advantages of using prompt-dependent language models with those of using a language model independent of the prompts generated by the dialogue system. The technique proposes to create a new speech recognizer, termed contextual speech recognizer, that uses a prompt-independent language model to allow recognizing any kind of sentence permitted in the application domain, and at the same time, uses contextual information (in the form of prompt-dependent language models) to take into account that some sentences are more likely to be uttered than others at a particular moment of the dialogue. The experiments show the technique allows enhancing clearly the performance of the input interface of a previously developed dialogue system based exclusively on prompt-dependent language models. But most important, in comparison with a standard speech recognizer that uses just one prompt-independent language model without contextual information, the proposed recognizer allows increasing the word accuracy and sentence understanding rates by 4.09% and 4.19% absolute, respectively. These scores are slightly better than those obtained using linear interpolation of the prompt-independent and prompt-dependent language models used in the experiments.
