By Sadaoki Furui (auth.), Professor Laila Dybkjær, Holmer Hemsen, Professor Wolfgang Minker (eds.)
In its 9 chapters, this publication offers an outline of the cutting-edge and most sensible perform in different sub-fields of evaluate of textual content and speech platforms and parts. The overview points coated comprise speech and speaker popularity, speech synthesis, lively conversing brokers, part-of-speech tagging, parsing, and ordinary language software program like desktop translation, details retrieval, query answering, spoken discussion structures, info assets, and annotation schemes. With its huge assurance and unique contributions this booklet is exclusive within the box of assessment of speech and language expertise.
This ebook is of specific relevance to complicated undergraduate scholars, PhD scholars, educational and business researchers, and practitioners.
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Additional info for Evaluation of Text and Speech Systems
And Young, S. J. (1993). HMM Recognition in Noise Using Parallel Model Combination. In Proceedings of the European Conference on Speech Communication and Technology (Eurospeech), volume 2, pages 837–840, Berlin, Germany. , editors (1998). Spoken Language System Assessment. Handbook of Standards and Resources for Spoken Language Systems, volume 3. Mouton de Gruyter, Berlin, Germany. , and Porter, J. (1991). Speaker Verification Using Randomized Phrase Prompting. Digital Signal Processing, 1:89–106.
Difficulty in automatic speaker recognition varies depending on whether or not the speakers intend to have their identities verified. During actual speaker verification, speakers are usually expected to cooperate without intentionally changing their speaking rate or manner. It is well known, however, and expected that speakers are most often uncooperative in criminal investigations, consequently compounding the difficulty in correctly recognizing their voices. Both text-dependent and text-independent methods have a serious weakness.
Consequently, the effectiveness of speaker identification systems must be evaluated according to their targeted population size. 3 Long-Term Variability of Speech As described in the previous section, even if the same words or sentences are spoken by the same speaker, speech characteristics are always varying, and there are also long-term trends. Samples recorded together in one session are much more highly correlated than those recorded in separate sessions. Therefore, the number of training sessions for making speaker models or templates and the time interval between those sessions, as well as training and testing 22 EVALUATION OF TEXT AND SPEECH SYSTEMS sessions, are important factors.