In this paper, we explore a text type based scheme of text analysis, through the specific problem of detecting the list text type. This is important because TTS systems that can generate the very distinct F0 contour of lists sound more natural. The presented list detection algorithm uses part-of-speech tags as input, and detects lists by computing the alignment costs of clauses in a sentence. T...

This paper presents a stochastic model of intonation contours for use in text-to-speech synthesis. The model has two modules, a linguistic module that generates abstract prosodic labels from text, and a phonetic module that generates an F0 curve from the abstract prosodic labels. This model differs from previous work in the abstract prosodic labels used, which can be automatically derived from ...

We used four Part-of-Speech taggers, which are available for research purposes and were originally trained on text to tag a corpus of transcribed multiparty spoken dialogues. The assigned tags were then manually corrected. The correction was first used to evaluate the four taggers, then to retrain them. Despite limited resources in time, money and annotators we reached results comparable to tho...

There are different approaches to the problem of assigning each word of a text with a parts-of-speech tag, which is known as Part-Of-Speech (POS) tagging. In this paper we compare the performance of a few POS tagging techniques for Bangla language, e.g. statistical approach (n-gram, HMM) and transformation based approach (Brill’s tagger). A supervised POS tagging approach requires a large amoun...

We introduce a simple method to build Lexicalized Hidden Markov Models (L-HMMs) for improving the precision of part-of-speech tagging. This technique enriches the contextual Language Model taking into account a set of selected words empirically obtained. The evaluation was conducted with different lexicalization criteria on the Penn Treebank corpus using the TnT tagger. This lexicalization obta...

We present new statistical models for jointly labeling multiple sequences and apply them to the combined task of partof-speech tagging and noun phrase chunking. The model is based on the Factorial Hidden Markov Model (FHMM) with distributed hidden states representing partof-speech and noun phrase sequences. We demonstrate that this joint labeling approach, by enabling information sharing betwee...

This paper examines tagging models for spontaneous English speech transcripts. We analyze the performance of state-of-the-art tagging models, either generative or discriminative, left-to-right or bidirectional, with or without latent annotations, together with the use of ToBI break indexes and several methods for segmenting the speech transcripts (i.e., conversation side, speaker turn, or human...

We present an approach to statistical partof-speech tagging that uses two di erent tagsets, one for its internal and one for its external representation. The internal tagset is used in the underlying Markov model, while the external tagset constitutes the output of the tagger. The internal tagset can be modi ed and optimized to increase tagging accuracy (with respect to the external tagset). We...