[PDF] Top 20 A New Bigram-PLSA Language Model for Speech Recognition

A New Bigram-PLSA Language Model for Speech Recognition

... the PLSA and Nie et al.’s bigram-PLSA models were trained by the same data employed to train our proposed bigram-PLSA ...the PLSA and Nie et al.’s bigram-PLSA ... See full document

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A Novel Modeling Method for Acoustic Model in Deep Neural Network by Introducing Language Vector

... single-language speech recognition system cannot satisfy the increasingly diversified world, so the multi-language or cross-language speech recognition system is paid more ... See full document

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Building bilingual lexicon to create Dialect Tunisian corpora and adapt language model

... natural language processing (NLP): since the spoken dialects are not officially written and do not have standard orthography, it is very costly to obtain adequate corpora to use for training NLP ...create ... See full document

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Recurrent neural network language model adaptation for multi-genre broadcast speech recognition and alignment

... network language models (RNNLMs) generally outperform n -gram language models when used in automatic speech ...to new domains is an open problem and current approaches can be categorised as ... See full document

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Dual supervised learning for non-native speech recognition

... each model is crucial, because each of them is responsible for either synthesizing new data sam- ples, evaluating the results yielded by the previous model, or converting the data between the textual ... See full document

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Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech

... n-gram language model, their effectiveness in real-life ASR tasks has never been systematically investigated so ...predicting new words (Kuhn and De Mori, 1990; Lau et ... See full document

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An enhanced hybrid DBN/HMM for Tamil language speech recognition system

... of speech recognition system. The HMM/MLP, hybrid approach speech recognition system, uses MLP to estimate phone class probabilities, that are utilized in an observation probabilities for a ... See full document

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Interpolated Dirichlet Class Language Model for Speech Recognition Incorporating Long distance N grams

... LSA, PLSA and LDA models have been used successfully in recent research work for LM adaptation (Bellegarda, 2000; Gildea and Hofmann, 1999; Mrva and Woodland, 2004; Tam and Schultz, 2005; Tam and Schultz, 2006; ... See full document

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Phone lattice reconstruction for embedded language recognition in LVCSR

... dedicated language identi- fier which runs separately before any other processing steps take ...with speech recognition in an assumed core language, restarting the recognition process ... See full document

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TINA: A Natural Language System for Spoken Language Applications

... 7 A bigram language model is commonly used in speech recognition systems, where bigram statistics frequency counts on adjacent word pairs are collected from words or word categories in s[r] ... See full document

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Observations from Statistical Processing of BDNC01 Corpus

... the language or languages or language varieties of the corpus, the location of texts, the date or period of texts ...printed language, e-mails and web pages as compared with the labor and expense of ... See full document

7

Various Applications of Digital Signal Processing DSP

... communication. Speech is a one dimensional ...characterizing speech, one of which according to information theory is to represent the speech in terms of its message or information ...represent ... See full document

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RNN language model with word clustering and class-based output layer

... One key issue is the heavy computational cost for the RNNLM. As the output layer contains one unit for each word in the vocabulary, it is infeasible to train the model for large vocabulary with hundreds of ... See full document

7

Intelligence Agent Device for E Learning

... the speech signal as quasistatic for short durations and models these frames for ...generate speech (sequences of cepstral vectors) using a number of states for each model and modeling the short- ... See full document

5

EMNLP versus ACL: Analyzing NLP research over time

... Algorithm, Model, Optimization Problem, Large Number 7 Language, Information, Sentence, System, Results, Corpus, Approach, Research, Learning, Language Processing, Systems, Machine 8 Rules, Parse ... See full document

5

Design of a Tigrinya Language Speech Corpus for Speech Recognition

... with new details about the languages (language of the recording, mother tongue of the speaker, other languages spoken) and about the speaker (name, age, gender, region of ... See full document

5

Continuous Speech Recognition for Punjabi Language

... A phonetically balanced pronunciation dictionary is required to compile speech audio & transcriptions into acoustic model. Lexicon preparation has also been given due care so that all the phones of ... See full document

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Audio Visual Speech Synthesis and Speech Recognition for Hindi Language

... expressive speech is a target application with potential relevance in several areas, including the dynamic generation of multimodal media content and naturalistic human–machine ...the speech waveform[15]. ... See full document

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An Efficient A* Stack Decoder Algorithm for Continuous Speech Recognition with a Stochastic Language Model

... An Efficient A* Stack Decoder Algorithm for Continuous Speech Recognition with a Stochastic Language Model A n E f f i c i e n t A * S t a c k D e c o d e r A l g o r i t h m for C o n t i n u o u s S[.] ... See full document

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Speech Recognition Using a Stochastic Language Model Integrating Local and Global Constraints

... SPEECH RECOGNITION USING A STOCHASTIC LANGUAGE MODEL INTEGRATING LOCAL AND GLOBAL CONSTRAINTS S P E E C H R E C O G N I T I O N U S I N G A S T O C H A S T I C L A N G U A G E M O D E L I N T E G R A[.] ... See full document

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