3,499 research outputs found
Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data
Audio Word2Vec offers vector representations of fixed dimensionality for
variable-length audio segments using Sequence-to-sequence Autoencoder (SA).
These vector representations are shown to describe the sequential phonetic
structures of the audio segments to a good degree, with real world applications
such as query-by-example Spoken Term Detection (STD). This paper examines the
capability of language transfer of Audio Word2Vec. We train SA from one
language (source language) and use it to extract the vector representation of
the audio segments of another language (target language). We found that SA can
still catch phonetic structure from the audio segments of the target language
if the source and target languages are similar. In query-by-example STD, we
obtain the vector representations from the SA learned from a large amount of
source language data, and found them surpass the representations from naive
encoder and SA directly learned from a small amount of target language data.
The result shows that it is possible to learn Audio Word2Vec model from
high-resource languages and use it on low-resource languages. This further
expands the usability of Audio Word2Vec.Comment: arXiv admin note: text overlap with arXiv:1603.0098
Spoken Language Intent Detection using Confusion2Vec
Decoding speaker's intent is a crucial part of spoken language understanding
(SLU). The presence of noise or errors in the text transcriptions, in real life
scenarios make the task more challenging. In this paper, we address the spoken
language intent detection under noisy conditions imposed by automatic speech
recognition (ASR) systems. We propose to employ confusion2vec word feature
representation to compensate for the errors made by ASR and to increase the
robustness of the SLU system. The confusion2vec, motivated from human speech
production and perception, models acoustic relationships between words in
addition to the semantic and syntactic relations of words in human language. We
hypothesize that ASR often makes errors relating to acoustically similar words,
and the confusion2vec with inherent model of acoustic relationships between
words is able to compensate for the errors. We demonstrate through experiments
on the ATIS benchmark dataset, the robustness of the proposed model to achieve
state-of-the-art results under noisy ASR conditions. Our system reduces
classification error rate (CER) by 20.84% and improves robustness by 37.48%
(lower CER degradation) relative to the previous state-of-the-art going from
clean to noisy transcripts. Improvements are also demonstrated when training
the intent detection models on noisy transcripts
Temporal Attention-Gated Model for Robust Sequence Classification
Typical techniques for sequence classification are designed for
well-segmented sequences which have been edited to remove noisy or irrelevant
parts. Therefore, such methods cannot be easily applied on noisy sequences
expected in real-world applications. In this paper, we present the Temporal
Attention-Gated Model (TAGM) which integrates ideas from attention models and
gated recurrent networks to better deal with noisy or unsegmented sequences.
Specifically, we extend the concept of attention model to measure the relevance
of each observation (time step) of a sequence. We then use a novel gated
recurrent network to learn the hidden representation for the final prediction.
An important advantage of our approach is interpretability since the temporal
attention weights provide a meaningful value for the salience of each time step
in the sequence. We demonstrate the merits of our TAGM approach, both for
prediction accuracy and interpretability, on three different tasks: spoken
digit recognition, text-based sentiment analysis and visual event recognition.Comment: Accepted by CVPR 201
ASR error management for improving spoken language understanding
This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic information system. An approach is proposed for enriching the set of
semantic labels with error specific labels and by using a recently proposed
neural approach based on word embeddings to compute well calibrated ASR
confidence measures. Experimental results are reported showing that it is
possible to decrease significantly the Concept/Value Error Rate with a state of
the art system, outperforming previously published results performance on the
same experimental data. It also shown that combining an SLU approach based on
conditional random fields with a neural encoder/decoder attention based
architecture , it is possible to effectively identifying confidence islands and
uncertain semantic output segments useful for deciding appropriate error
handling actions by the dialogue manager strategy .Comment: Interspeech 2017, Aug 2017, Stockholm, Sweden. 201
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