[PDF] Top 20 Classifying Recognition Results for Spoken Dialog Systems

... We are aware that this might not be an optimal setting. Some spoken dialog systems only spot for keywords or key-phrases in an utterance. For them it does not matter whether “unimportant” words were ... See full document

... words on our “synonymously used words” list by using the Levenshtein distance. Third, after decid- ing which spelling is the most appropriate one for each word, we store the corrected utterances and use them for further ... See full document

... In spoken dialog systems, statistical state tracking aims to improve robustness to speech recognition errors by tracking a posterior distribution over hidden dialog ...with ... See full document

... An Adaptive User Model for Turn Length. We learned from our experiments and related work that turn length is important to control. Although we found that our system tended to produce turns that were often rather complex, ... See full document

... speech recognition technology, spoken dialog systems (SDSs) with which a user interacts through natural speech has been widely used in human-computer ...speech recognition (ASR) ... See full document

... speech recognition (ASR) system has to process words, which are not in the lexicon of the speech recognizer (Klakow et ...phoneme-based recognition is the establish- ment of corresponding best rated ... See full document

... raw dialog history and gen- erate the next system utterance using a separate RNN ...KB results that occurred in the training data would produce false ... See full document

... include dialog state in- formation, turn-taking features, such as whether the current user utterance is a barge-in, and semantic information derived from the dialog state and par- tial recognition ... See full document

... the dialog include whether the system uses implicit or explicit confirmation or some combination of both, whether the system has an open-ended first turn or a directed one, and whether it deals with requests for ... See full document

... tion results for SPaRKy (1) support the results for SPoT, by showing that trainable sentence generation can produce output comparable to template-based generation, even for complex in- formation ... See full document

... This paper deals with an incremental turn- taking model that provides a novel solution for end-of-turn detection. It includes a flex- ible framework that enables active system barge-in. In order to accomplish this, a ... See full document

... analysis results confirm that people adapt their speaking style depending on whom they are talking ...freely spoken user input should not be considered synonymous with human-directed speech, namely with ... See full document

... While spoken dialog systems have the poten- tial to remove data error that is introduced by variation in human interviewer behaviors, they also introduce risks to survey data quality due to speech ... See full document

... managing spoken dialog systems is through the use of finite-state transducers (FSTs), a method which has been studied and developed by several ...speech recognition, spoken language ... See full document

... in dialog systems has addressed simulation techniques towards the goal of training and ...select dialog strate- gies in clarification ...of dialog systems (Hone and Baber, 1995; Araki ... See full document

... Recent spoken dialog systems have been able to recognize freely spoken user input in restricted domains thanks to statistical methods in the automatic speech ... See full document

... DA recognition is to split a sequence of words into segments, each of which corresponds to one DA ...ken dialog systems, NLU is based on Automatic Speech Recognition (ASR) hypotheses or tran- ... See full document

... similar results that are better than the model trained on ConvBank ...similar results, we fine-tune on the slightly better-performing EWT ...These results suggest leveraging both a large set of ... See full document

... Like most working research spoken dialog systems, AMELIA uses hand-crafted, template- based generation. Its output is created by choos- ing string templates for each elementary speech act, using a ... See full document

... domain semantic knowledge resources are use- ful for text classification problems. Their success in limited data scenario is an attractive prospect, since most dialog agents operate in scarce train- ing data ... See full document