177 research outputs found

    Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean

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    A new tightly coupled speech and natural language integration model is presented for a TDNN-based continuous possibly large vocabulary speech recognition system for Korean. Unlike popular n-best techniques developed for integrating mainly HMM-based speech recognition and natural language processing in a {\em word level}, which is obviously inadequate for morphologically complex agglutinative languages, our model constructs a spoken language system based on a {\em morpheme-level} speech and language integration. With this integration scheme, the spoken Korean processing engine (SKOPE) is designed and implemented using a TDNN-based diphone recognition module integrated with a Viterbi-based lexical decoding and symbolic phonological/morphological co-analysis. Our experiment results show that the speaker-dependent continuous {\em eojeol} (Korean word) recognition and integrated morphological analysis can be achieved with over 80.6% success rate directly from speech inputs for the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer processing of oriental language journa

    Fast and Accurate OOV Decoder on High-Level Features

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    This work proposes a novel approach to out-of-vocabulary (OOV) keyword search (KWS) task. The proposed approach is based on using high-level features from an automatic speech recognition (ASR) system, so called phoneme posterior based (PPB) features, for decoding. These features are obtained by calculating time-dependent phoneme posterior probabilities from word lattices, followed by their smoothing. For the PPB features we developed a special novel very fast, simple and efficient OOV decoder. Experimental results are presented on the Georgian language from the IARPA Babel Program, which was the test language in the OpenKWS 2016 evaluation campaign. The results show that in terms of maximum term weighted value (MTWV) metric and computational speed, for single ASR systems, the proposed approach significantly outperforms the state-of-the-art approach based on using in-vocabulary proxies for OOV keywords in the indexed database. The comparison of the two OOV KWS approaches on the fusion results of the nine different ASR systems demonstrates that the proposed OOV decoder outperforms the proxy-based approach in terms of MTWV metric given the comparable processing speed. Other important advantages of the OOV decoder include extremely low memory consumption and simplicity of its implementation and parameter optimization.Comment: Interspeech 2017, August 2017, Stockholm, Sweden. 201

    Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages

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    This paper provides an overall introduction of our Automatic Speech Recognition (ASR) systems for Southeast Asian languages. As not much existing work has been carried out on such regional languages, a few difficulties should be addressed before building the systems: limitation on speech and text resources, lack of linguistic knowledge, etc. This work takes Bahasa Indonesia and Thai as examples to illustrate the strategies of collecting various resources required for building ASR systems.Comment: Published by the 2017 IEEE International Conference on Orange Technologies (ICOT 2017

    An acoustic-phonetic approach in automatic Arabic speech recognition

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    In a large vocabulary speech recognition system the broad phonetic classification technique is used instead of detailed phonetic analysis to overcome the variability in the acoustic realisation of utterances. The broad phonetic description of a word is used as a means of lexical access, where the lexicon is structured into sets of words sharing the same broad phonetic labelling. This approach has been applied to a large vocabulary isolated word Arabic speech recognition system. Statistical studies have been carried out on 10,000 Arabic words (converted to phonemic form) involving different combinations of broad phonetic classes. Some particular features of the Arabic language have been exploited. The results show that vowels represent about 43% of the total number of phonemes. They also show that about 38% of the words can uniquely be represented at this level by using eight broad phonetic classes. When introducing detailed vowel identification the percentage of uniquely specified words rises to 83%. These results suggest that a fully detailed phonetic analysis of the speech signal is perhaps unnecessary. In the adopted word recognition model, the consonants are classified into four broad phonetic classes, while the vowels are described by their phonemic form. A set of 100 words uttered by several speakers has been used to test the performance of the implemented approach. In the implemented recognition model, three procedures have been developed, namely voiced-unvoiced-silence segmentation, vowel detection and identification, and automatic spectral transition detection between phonemes within a word. The accuracy of both the V-UV-S and vowel recognition procedures is almost perfect. A broad phonetic segmentation procedure has been implemented, which exploits information from the above mentioned three procedures. Simple phonological constraints have been used to improve the accuracy of the segmentation process. The resultant sequence of labels are used for lexical access to retrieve the word or a small set of words sharing the same broad phonetic labelling. For the case of having more than one word-candidates, a verification procedure is used to choose the most likely one

    The recognition of New Zealand English closing diphthongs using time-delay neural networks

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    As a step towards the development of a modular time-delay neural network (TDNN) for recognizing phonemes realized with a New Zealand English accent, this thesis focuses on the development of an expert module for closing diphthong recognition. The performances of traditional and squad-based expert modules are compared speaker-dependently for two New Zealand English speakers (one male and one female). Examples of each kind of expert module are formed from one of three types of TDNN, referred to as basic-token TDNN, extended-token TDNN and sequence-token TDNN. Of the traditional expert modules tested, those comprising extended-token TDNNs are found to afford the best performance compromises and are, therefore, preferable if a traditional expert module is to be used. Comparing the traditional and squad-based expert modules tested, the latter afford significantly better recognition and/or false-positive error performances than the former, irrespective of the type of TDNN used. Consequently, it is concluded that squad-based expert modules are preferable to their traditional counterparts for closing diphthong recognition. Of the squad-based expert modules tested, those comprising sequence-token TDNNs are found to afford consistently better false-positive error performances than those comprising basic- or extended-token TDNNs, while similar recognition performances are afforded by all. Consequently, squad-based expert modules comprising sequence-token TDNNs are recommended as the preferred method of recognizing closing diphthongs realized with a New Zealand accent. This thesis also presents results demonstrating that squad-based expert modules comprising sequence-token TDNN s may be trained to accommodate multiple speakers and in a manner capable of handling both uncorrupted and highly corrupted speech utterances
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