Learn More
We address an adaptation method of statistical language models to topics and speaker characteristics for automatic transcription of meetings and discussions. A baseline language model is a mixture of two models, which are trained with different corpora covering various topics and speakers, respectively. Then, probabilistic latent semantic analysis (PLSA) is(More)
One of the most significant problems in language modeling of spontaneous speech such as meetings and lectures is that only limited amount of matched training data, i.e. faithful transcript for the relevant task domain, is available. In this paper, we propose a novel transformation approach to estimate language model statistics of spontaneous speech from a(More)
For effective training of acoustic and language models for spontaneous speech such as meetings, it is significant to exploit the texts available in a large scale, which may not be faithful transcripts of the utterances. We have proposed a language model transformation scheme to cope with the differences between verbatim transcripts of spontaneous utterances(More)
The paper addresses language model adaptation for automatic lecture transcription by fully exploiting presentation slide information used in the lecture. As the text in the presentation slides is small in its size and fragmentary in its content, a robust adaptation scheme is addressed by focusing on the keyword and topic information. Several methods are(More)
We propose a novel approach based on a statistical transformation framework for language and pronunciation modeling of spontaneous speech. Since it is not practical to train a spoken-style model using numerous spoken transcripts, the proposed approach generates a spoken-style model by transforming an orthographic model trained with document archives such as(More)
This paper presents two different approaches utilizing statistical language model (SLM) and support vector machines (SVM) for sentence boundary detection of spontaneous Japanese. In the SLM-based approach, linguistic likelihoods and occurrence of pause are used to determine sentence boundaries. To suppress false alarms, heuristic patterns of end-of-sentence(More)
We present unsupervised speaker indexing combined with automatic speech recognition (ASR) for speech archives such as discussions. Our proposed indexing method is based on anchor models, by which we define a feature vector based on the similarity with speakers of a large scale speech database. Several techniques are introduced to improve discriminant(More)
This paper presents an automatic speech recognition (ASR) system for assisting meeting record creation of the National Congress of Japan. The system is designed to cope with spontaneous characteristics of meeting speech, as well as a variety of topics and speakers. For acoustic model, minimum phone error (MPE) training is applied with several normalization(More)
For language modeling of spontaneous speech, we propose a novel approach, based on the statistical machine translation framework, which transforms a document-style model to the spoken style. For better coverage and more reliable estimation, incorporation of POS (part-of-speech) information is explored in addition to lexical information. In this paper, we(More)
Automatic speech recognition (ASR) results contain not only ASR errors, but also disfluencies and colloquial expressions that must be corrected to create readable transcripts. We take the approach of statistical machine translation (SMT) to “translate” from ASR results into transcript-style text. We introduce two novel modeling techniques in(More)