A novel framework of robust speech understanding is presented. It is based on a detection and verification strategy. It extracts the semantically significant parts and rejects the irrelevant parts rather than decoding the whole utterances. There are two key features in our strategy. Firstly, discriminative verifier is integrated to suppress false alarms. It uses anti-subword models specifically trained to verify the recognition results. The second feature is the use of a keyphrase network as the detection unit. It embeds stochastic constraint of keyword and keyphrase connections to improve the coverage and detection rates. The automatic generation of the keyphrase network structure is also addressed. This topdown variable-length language model can be trained with a small corpus and ported to different tasks. This property coupled with the vocabulary-independent detector and verifier enhances the portability of our framework. As spoken language systems or spoken dialogue systems are being evaluated for wider usage in real-world applications, it is found that they are not sufficient to cope with the utterance variation inherent in a large user population. The conventional systems try to decode the whole utterance with the k e d vocabulary and language models and match every part of the input with some words uniformly. The use of a rigid grammar is effective for typical in-grammar sentence patterns. However, in real-world environments, we have observed a large number of out-of-grammar utterances even after the task grammars had been tuned by human experts during the trial period. These samples include extraneous words, hesitations, repairs and unexpected expressions. On the other hand, most of the mis-recognized utterances contain some keyphrases that are task-related and may lead to partial or full understanding. Flexible speech understanding should be able to detect semantically significant parts and reject irrelevant parts. In a specific task domain of a transaction or information retrieval system, it is possible to make sense with keywords or keyphrases. Therefore, the a p p r o d based on their detection is attractive. By relaxing the grammatical constraints and focusing on the keywords and keyphrases, it will accept a wider variety of utterances than sentence grammars can. Monnation Science, Kyoto University, Kyoto 60&01, Japan 'This work has been carried out while visiting from Dept. of Especially in dialogue-based systems, it is possible to set up grammars according to the dialogue state and apply to a large-scale task. Combined with a flexible dialogue manager, the detection-based approach realizes partial understanding and disambiguation of unclear parts through the following dialogue session. In this paper, we propose a keyphrase detection approach that realizes flexible understanding. Based on a review of the conventional word spotting approaches, which often suffers from excessive false alarms and is hard to apply to a variety of tasks, our framework features the discriminative verification and the keyphrase network structure to improve the detection performance and the system portability. The outline of our detection-based speech understanding system consists of the following steps. 1. keyphrase detection A set of keyphrases axe detected using a set of phrase sub-grammars s p d c to the dialogue state. The keyphrases are labeled with semantic tags, which are useful in the sentence-level parsing. keyphrase verification The detected keyphrases are verified and assigned confidence measures. The process eliminates false alarms and rescores the verified candidates. The verifier is constructed with anti-subword models which test the individual subwords of the recognized results. sentence parsing The keyphrase candidates are connected into sentence hypotheses using the task-specific semantic knowledge sources. A stack decoder is used to search the optimal hypotheses that satisfy the semantic constraints [l]. sentence rescoring The semantically valid sentence hypotheses are verified and rescored with detailed d d c a t i o n and verification models by reprocessing the speech input. Step 1 and 2 are performed simultaneously with the respective modules. Detail of these steps are discussed in the following sections, as they are novel parts of our framework. 86 1
In this method REF , the anti-subword model is used, and a key-phrase network is used as the detection unit.
Key-phrase detection and verification for flexible speech understanding
Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96
Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96
Computer Science