We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequences---a domain in which universal symbol inventories and cross-linguistically shared feature representations are a natural fit. Intrinsic evaluation on held-out perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show (i) that polyglot models better generalize to held-out data than comparable monolingual models and (ii) that polyglot phonetic feature representations are of higher quality than those learned monolingually.
Interpolation of monolingual LMs is an alternative to obtain a multilingual model @cite_36 @cite_45 . However, interpolated models still require a trained model per language, and do not allow parameter sharing at training time. Bilingual language models trained on concatenated corpora were explored mainly in speech recognition @cite_46 @cite_23 @cite_50 . Adaptations have been proposed to apply language models in bilingual settings in machine translation @cite_16 and code switching @cite_38 . These approaches, however, require adaptation to every pair of languages, and an adapted model cannot be applied to more than two languages.
@cite_38 @cite_36 @cite_45 @cite_50 @cite_23 @cite_46 @cite_16
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In this paper, we investigate the application of recurrent neural network language models (RNNLM) and factored language models (FLM) to the task of language modeling for Code-Switching speech. We present a way to integrate partof-speech tags (POS) and language information (LID) into these models which leads to significant improvements in terms of perplexity. Furthermore, a comparison between RNNLMs and FLMs and a detailed analysis of perplexities on the different backoff levels are performed. Finally, we show that recurrent neural networks and factored language models can be combined using linear interpolation to achieve the best performance. The final combined language model provides 37.8 relative improvement in terms of perplexity on the SEAME development set and a relative improvement of 32.7 on the evaluation set compared to the traditional n-gram language model. A method for speech recognition. The method uses a single pronunciation estimator to train acoustic phoneme models and recognize utterances from multiple languages. The method includes accepting text spellings of training words in a plurality of sets of training words, each set corresponding to a different one of a plurality of languages. The method also includes, for each of the sets of training words in the plurality, receiving pronunciations for the training words in the set, the pronunciations being characteristic of native speakers of the language of the set, the pronunciations also being in terms of subword units at least some of which are common to two or more of the languages. The method also includes training a single pronunciation estimator using data comprising the text spellings and the pronunciations of the training words. This paper describes our work in developing multilingual (Swedish and English) speech recognition systems in the ATIS domain. The acoustic component of the multilingual systems is realized through sharing Gaussian codebooks across Swedish and English allophones. The language model (LM) components are constructed by training a statistical bigram model, with a common backoff node, on bilingual texts, and by combining two monolingual LMs into a probabilistic finite state grammar. This system uses a single decoder for Swedish and English sentences, and is capable of recognizing sentences with words from both languages. Preliminary experiments show that sharing acoustic models across the two languages has not resulted in improved performance, while sharing a backoff node at the LM component provides flexibility and ease in recognizing bilingual sentences at the expense of a slight increase in word error rate in some cases. As a by-product, the bilingual decoder also achieves good performance on language identification (LID). The increasing interest in multilingual applications like speech-to-speech translation systems is accompanied by the need for speech recognition front-ends in many languages that can also handle multiple input languages at the same time. In this paper we describe a universal speech recognition system that fulfills such needs. It is trained by sharing speech and text data across languages and thus reduces the number of parameters and overhead significantly at the cost of only slight accuracy loss. The final recognizer eases the burden of maintaining several monolingual engines, makes dedicated language identification obsolete and allows for code-switching within an utterance. To achieve these goals we developed new methods forconstructing multilingual acoustic models and multilingual n-gram language models. In this paper we describe our initial e orts in building a natural language understanding (NLU) system across multiple languages. The system allows users to switch languages seamlessly in a single session without requiring any switch in the speech recognition system. Context dependence is maintained across sentences, even when the user changes languages. Towards this end we have begun building a universal speech recognizer for English and French languages. We experiment with a universal phonology for both French and English with a novel mechanism to handle language dependent variations. Our best results so far show about 5 relative performance degradation for English relative to a unilingual English system and a 9 relative degradation in French relative to a unilingual French system. The NLU system uses the same statistical understanding algorithms for each language, making system development, maintenance, and portability vastly superior to systems built customly for each language. In past Evaluations for Machine Translation of European Languages, it could be shown that the translation performance of SMT systems can be increased by integrating a bilingual language model into a phrase-based SMT system. In the bilingual language model, target words with their aligned source words build the tokens of an n-gram based language model. We analyzed the effect of bilingual language models and show where they could help to better model the translation process. We could show improvements of translation quality on German-to-English and Arabic-to-English. In addition, for the Arabic-to-English task, training an extra bilingual language model on the POS tags instead of the surface word forms led to further improvements.