Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on Cognitive Systems

Perception of linguistic rhythm by newborn infants

Previous studies have shown that newborn infants are able to discriminate between certain languages, and it has been suggested that they do so by categorizing varieties of speech rhythm. However, in order to confirm this hypothesis, it is necessary to show that language discrimination is still performed by newborns when all speech cues other than rhythm are removed. Here, we conducted a series of experiments assessing discrimination between Dutch and Japanese by newborn infants, using a speech resynthesis technique to progressively degrade non-rhythmical properties of the sentences. When the stimuli are resynthesized using identical phonemes and artificial intonation contours for the two languages, thereby preserving only their rhythmic structure, newborns are still able to discriminate the languages. We conclude that new-borns are able to classify languages according to their type of rhythm, and that this ability may help them bootstrap other phonological properties of their native language

Using closely-related language to build an ASR for a very under-resourced language: Iban

International audienceThis paper describes our work on automatic speech recognition system (ASR) for an under-resourced language, Iban, a language that is mainly spoken in Sarawak, Malaysia. We collected 8 hours of data to begin this study due to no resources for ASR exist. We employed bootstrapping techniques involving a closely-related language for rapidly building and improve an Iban system. First, we used already available data from Malay, a local dominant language in Malaysia, to bootstrap grapheme-to-phoneme system (G2P) for the target language. We also built various types of G2Ps, including a grapheme-based and an English G2P, to produce different versions of dictionaries. We tested all of the dictionaries on the Iban ASR to provide us the best version. Second, we improved the baseline GMM system word error rate (WER) result by utilizing subspace Gaussian mixture models (SGMM). To test, we set two levels of data sparseness on Iban data; 7 hours and 1 hour transcribed speech. We investigated cross-lingual SGMM where the shared parameters were obtained either in monolingual or multilingual fashion and then applied to the target language for training. Experiments on out-of-language data, English and Malay, as source languages result in lower WERs when Iban data is very limited