Morpheme Based Language Models for Speech Recognition of Czech

We propose a method of dynamically registering out-of-vocabulary (OOV) words by assigning the pronunciations of these words to pre-inserted OOV tokens, editing the pronunciations of the tokens. To do this, we add OOV tokens to an additional, partial copy of our corpus, either randomly or to part-of-speech (POS) tags in the selected utterances, when training the language model (LM) for speech recognition. This results in an LM containing OOV tokens, to which we can assign pronunciations. We also investigate the impact of acoustic complexity and the “natural” occurrence frequency of OOV words on the recognition of registered OOV words. The proposed OOV word registration method is evaluated using two modern automatic speech recognition (ASR) systems, Julius and Kaldi, using DNN-HMM acoustic models and N-gram language models (plus an additional evaluation using RNN re-scoring with Kaldi). Our experimental results show that when using the proposed OOV registration method, modern ASR systems can recognize OOV words without re-training the language model, that the acoustic complexity of OOV words affects OOV recognition, and that differences between the “natural” and the assigned occurrence frequencies of OOV words have little impact on the final recognition results.

In this paper, we describe the work developed in the creation of syllable-based language model for continuous speech recognition system for Myanmar language. Speech recognition systems contain language model as the part of prediction word order sequence. In English and other languages, speech recognition system can use word-based language model. Since Myanmar is monosyllabic and syllable-timed language, the syllable-based language model is more suitable in speech recognition system. At the first step, this paper explains the structure of traditional (Phrase based) language model and describes how to normalized Myanmar sentences and building of normalized Myanmar language model. In the second step, the evaluation of the two language models is expressed. Finally, the comparison of these two language models with the perplexity values is described. The syllable-based Myanmar language model has lower perplexity value than the traditional language model. This language model can support the creation of word-based Myanmar language model.

Although n-gram models are still the de facto standard in language modeling for speech recognition, it has been shown that more sophisticated models achieve better accuracy by taking additional information, such as syntactic rules, semantic relations or domain knowledge into account. Unfortunately, most of the effort in developing such models goes into the implementation of handcrafted inference routines. What lacks is a generic mechanism to introduce background knowledge into a language model. We propose the use of dynamic Bayesian networks for this purpose. Dynamic Bayesian networks can be seen as a generalization of the n-gram models and hmms traditionally used in language modeling and speech recognition. Whereas those models use a single random variable to represent state, Bayesian networks can have any number of variables. As such they are particularly well-suited for the construction of models that take additional information into account. In this paper language modeling with belief networks is discussed. Examples of belief network implementations of well-known language models are given and a new model is presented that models dependencies between the content words in a sentence.KeywordsBayesian NetworkSpeech RecognitionLanguage ModelContent WordDynamic Bayesian NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Recurrent neural network language models (RNNLM) have become an increasingly popular choice for state-of-the-art speech recognition systems. RNNLMs are normally trained by minimizing the cross entropy (CE) using the stochastic gradient descent (SGD) algorithm. The SGD method only uses first-order derivatives and no higher order gradient information is used to consider the correlation between model parameters. It is unable to fully capture the curvature of the error cost function. This can lead to slow convergence in model training. In this paper, a limited-memory Broyden Fletcher Goldfarb Shannon (L-BFGS) based second order optimization technique is proposed for RNNLMs. This method efficiently approximates the matrix-vector product between the inverse Hessian and gradient vector via a recursion over past gradients with a compact memory requirement. Consistent perplexity and error rate reductions are obtained over the SGD method on two speech recognition tasks: Switchboard English and Babel Cantonese. A faster convergence and speed up in RNNLM training time was also obtained. Index Terms: recurrent neural network, language model, second order optimization, limited-memory BFGS, speech recognition

Speech-recognition systems must often decide between competing ways of breaking up the acoustic input into strings of words. Since the possible strings may be acoustically similar, a language model is required; given a word string, the model returns its linguistic probability. Several Markov language models are discussed. A novel kind of language model which reflects short-term patterns of word use by means of a cache component (analogous to cache memory in hardware terminology) is presented. The model also contains a 3g-gram component of the traditional type. The combined model and a pure 3g-gram model were tested on samples drawn from the Lancaster-Oslo/Bergen (LOB) corpus of English text. The relative performance of the two models is examined, and suggestions for the future improvements are made. >

Topic tracking language model for speech recognition

Speech recognition in digital assistants such as Google Assistant can potentially benefit from the use of conversational context consisting of user queries and responses from the agent. We explore the use of recurrent, Long Short-Term Memory (LSTM), neural language models (LMs) to model the conversations in a digital assistant. Our proposed methods effectively capture the context of previous utterances in a conversation without modifying the underlying LSTM architecture. We demonstrate a 4% relative improvement in recognition performance on Google Assistant queries when using the LSTM LMs to rescore recognition lattices.

Probability and statistics in computational linguistics, a brief review.- Three issues in modern language modeling.- Stochastic analysis of Structured Language Modeling.- Latent semantic language modeling for speech recognition.- Prosody modeling for automatic speech recognition and understanding.- Switching dynamic system models for speech articulation and acoustics.- Segmental HMMS: Modeling dynamics and underlying structure in speech.- Modelling graph-based observation spaces for segment-based speech recognition.- Towards robust and adaptive speech recognition models.- Graphical models and automatic speech recognition.- An introduction to Markov chain Monte Carlo methods.- Semiparametric filtering in speech processing.- List of workshop participants.

Our goal is to extract information from telephone call in order to route the call to one of number of destinations. We assume that we do not know a priori the vocabulary used in the application and so we use phonetic recognition followed by identification of salient phone sequences. In previous work, we showed that using separate language model during recognition for each route gave improved performance over using single model. However, this technique decodes each utterance in terms of the salient sequences of each call route, which leads to insertion and substitution errors that degrade performance. In this paper, we introduce the use of mixture language models for speech recognition in the context of call route classification. The benefit of technique can has the efficiency of multiple language models to get accurate recognition on salient phoneme sequences; on the other hand, it can give help in classification, even if the size of some call routes have just 50~60 utterances. It avoids building HMMs for some salient phoneme sequences to decide whether it is correct of occurring in the utterance.

In this paper, we introduce a document-specific context probabilistic latent semantic analysis (DCPLSA) model for speech recognition. This is an extension of a CPLSA model [1] where the probability of word is conditioned only on topics. The CPLSA model uses the bigram counts that are the number of appearances of the bigrams in the corpus. These counts are the sum of the bigram counts in different documents where they could appear to describe different topics. We encounter this problem in the CPLSA model and introduce the document-specific CPLSA model (DCPLSA) where the probability of a word is conditioned on both topic and document. We carried out experiments on a continuous speech recognition (CSR) task using the Wall Street Journal (WSJ) corpus and have seen that the proposed DCPLSA approach yields significant reduction in both perplexity and word error rate (WER) measurements over the other approaches used in the literature.

This paper proposes an architecture for real-time large vocabulary speech recognition on a mobile embedded device. The speech recognition system is based on Hidden Markov Model (HMM), which involves complex mathematical operations such as probability estimation and Viterbi decoding. This computational nature makes it power hungry and realtime recognition is not achieved by porting software solutions on embedded device. Our system architecture has a low power embedded processor and dedicated ASIC units for complex computations. These units operate at a low frequency of 50 MHz thus consuming low power. The system uses RAM for the intermediate values and flash memory to store acoustic and language models for speech recognition.

A study on language modeling for speech recognition

Discriminative language modeling (DLM) attempts to improve speech recognition performance by reranking the recognition hypotheses output from a baseline system. Most of the existing DLM methods assume that the reranking task can be treated as a linear discrimination problem and all testing utterances share the same parameter vector for reranking of hypotheses. However, the latter assumption sometimes results in a trained DLM model with weak generalizability and unsatisfactory performance. In view of this problem, we hence propose a relevance-based DLM (RDLM) framework that can efficiently infer the DLM model parameters of each testing utterance on-the-fly for better recognition performance. The structures and characteristics of the RDLM framework are extensively investigated, while the performance is thoroughly analyzed and verified by comparison with the existing DLM methods.

In large vocabulary continuous speech recognition (LVCSR) for agglutinative and inflectional languages, we encounter problems due to theoretically infinite full-word lexicon size. Sub-word lexicon units may be utilized to dramatically reduce the out-of-vocabulary rate in test data. One can develop language models based on sub-word units to perform LVCSR. However, it has not always been beneficial to use sub-word lexicon units, since shorter units have higher acoustic confusability among them and language model history is effectively shorter as compared to the history in full-word language models. To reduce the aforementioned problems, we propose using the longest possible sub-word units in our lexicon, namely half-words and full-words only. We also incorporate linguistic rules of half-word combination into our statistical language model. The language constraints are represented with a rule-based WFSM which can be combined with an N-gram language model to yield a better and smaller language model. We study the performance of the proposed system for Turkish LVCSR, when the language constraint takes the form of enforcing vowel harmony between stems and endings. We also introduce novel error-rate metrics that are more appropriate than word-error-rate for agglutinative languages. Using half-words with a bi-gram model yields a significant reduction in word-error-rate as compared to a bi-gram full-word model. In addition, combining a tri-gram half-word language model with the vowel-harmony WFSM improves the accuracy further when rescoring the bi-gram lattices.

Subspace Gaussian mixture based language modeling for large vocabulary continuous speech recognition