International Journal of Computer Processing of Languages

Spelling recognition provides alternative input method for computer systems as well as enhances a speech recognizer to cope with incorrectly recognized words and out-of-vocabulary words. This paper presents a general framework of Thai speech recognition enhanced with spelling recognition. Towards the implementation of Thai spelling recognition, Thai alphabets and their spelling methods are analyzed. A method based on hidden Markov models is proposed for constructing a Thai spelling recognition system from an existing continuous speech corpus. To compensate speed difference between spelling utterances and continuous speech utterances, the adjustment of utterance speed is taken into account. Two alternative language models, bigram and trigram, are used to investigate the performance of spelling recognition under three different environments: close-type, open-type and mix-type language models. Using the 1.25-times-stretched training utterances under the mix-type language model, the system achieves 87.37% correctness and 87.18% accuracy for bigram, and up to 91.12% correctness and 90.80% accuracy for trigram.

As agileTV, Nuance XML Platforms, Microsoft Speech server2004 are the projects in the news, there is still a demand for a better speech recognition engine in terms of WER. This article puts forward the acoustic signal quality requirement for efficient speech recognition. It reports that the major thrust is on the acoustics of speech recognition. It also envisages the performance of various speech recognition engines in the industry, the techniques adopted by them towards achieving the quality acoustic signal of the speaker for efficient results [in terms of Less Word Rate Error] and the impact of the external factors that makes it less robust in terms of attaining high quality speech signal. To tackle the problem we suggest an intelligent mechanism of modification in the regular Input speech signal format for significant improvement in speech recognition.

This paper describes a new approach to English to Bangla translation. The English to Bangla Translator was developed. This system (BANGANUBAD) takes a paragraph of English sentences as input sentences and produces equivalent Bangla sentences. The BANGANUBAD system comprises a preprocessor, morphological recursive parser, semantic parser using English word ontology for context disambiguation, an electronic lexicon associated with grammatical information and a discourse processor. It also employs a lexical disambiguation analyzer. This system does not rely on a stochastic approach. Rather, it is based on a special kind of amalgamated architecture of transformer and rule-based NLE architectures along with various linguistic knowledge components.

This paper presents a new approach for large vocabulary Arabic speech recognition based on exploiting the morphological structures of the Arabic language. In this model, word discrimination is achieved by a hybrid analysis scheme, where vowels are described in detail while consonants are classified according to broad phonetic classes. Different phonetic classification strategies are used to describe two large vocabulary lexicons. The results show that about 83% of the 10,000 test Arabic words can be uniquely represented by using 7 broad phonetic classes for consonants and six classes for vowels. In this case, the maximum number of words having the same phonetic labelling is 6. This paper summarises the results of ten different phonetic classification schemes and discusses their implication for a large vocabulary speech recognition system.