27,364 research outputs found
Language Identification Using Visual Features
Automatic visual language identification (VLID) is the technology of using information derived from the visual appearance and movement of the speech articulators to iden- tify the language being spoken, without the use of any audio information. This technique for language identification (LID) is useful in situations in which conventional audio processing is ineffective (very noisy environments), or impossible (no audio signal is available). Research in this field is also beneficial in the related field of automatic lip-reading. This paper introduces several methods for visual language identification (VLID). They are based upon audio LID techniques, which exploit language phonology and phonotactics to discriminate languages. We show that VLID is possible in a speaker-dependent mode by discrimi- nating different languages spoken by an individual, and we then extend the technique to speaker-independent operation, taking pains to ensure that discrimination is not due to artefacts, either visual (e.g. skin-tone) or audio (e.g. rate of speaking). Although the low accuracy of visual speech recognition currently limits the performance of VLID, we can obtain an error-rate of < 10% in discriminating between Arabic and English on 19 speakers and using about 30s of visual speech
Phonetic Temporal Neural Model for Language Identification
Deep neural models, particularly the LSTM-RNN model, have shown great
potential for language identification (LID). However, the use of phonetic
information has been largely overlooked by most existing neural LID methods,
although this information has been used very successfully in conventional
phonetic LID systems. We present a phonetic temporal neural model for LID,
which is an LSTM-RNN LID system that accepts phonetic features produced by a
phone-discriminative DNN as the input, rather than raw acoustic features. This
new model is similar to traditional phonetic LID methods, but the phonetic
knowledge here is much richer: it is at the frame level and involves compacted
information of all phones. Our experiments conducted on the Babel database and
the AP16-OLR database demonstrate that the temporal phonetic neural approach is
very effective, and significantly outperforms existing acoustic neural models.
It also outperforms the conventional i-vector approach on short utterances and
in noisy conditions.Comment: Submitted to TASL
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