496 research outputs found
DNN adaptation by automatic quality estimation of ASR hypotheses
In this paper we propose to exploit the automatic Quality Estimation (QE) of
ASR hypotheses to perform the unsupervised adaptation of a deep neural network
modeling acoustic probabilities. Our hypothesis is that significant
improvements can be achieved by: i)automatically transcribing the evaluation
data we are currently trying to recognise, and ii) selecting from it a subset
of "good quality" instances based on the word error rate (WER) scores predicted
by a QE component. To validate this hypothesis, we run several experiments on
the evaluation data sets released for the CHiME-3 challenge. First, we operate
in oracle conditions in which manual transcriptions of the evaluation data are
available, thus allowing us to compute the "true" sentence WER. In this
scenario, we perform the adaptation with variable amounts of data, which are
characterised by different levels of quality. Then, we move to realistic
conditions in which the manual transcriptions of the evaluation data are not
available. In this case, the adaptation is performed on data selected according
to the WER scores "predicted" by a QE component. Our results indicate that: i)
QE predictions allow us to closely approximate the adaptation results obtained
in oracle conditions, and ii) the overall ASR performance based on the proposed
QE-driven adaptation method is significantly better than the strong, most
recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201
The Zero Resource Speech Challenge 2017
We describe a new challenge aimed at discovering subword and word units from
raw speech. This challenge is the followup to the Zero Resource Speech
Challenge 2015. It aims at constructing systems that generalize across
languages and adapt to new speakers. The design features and evaluation metrics
of the challenge are presented and the results of seventeen models are
discussed.Comment: IEEE ASRU (Automatic Speech Recognition and Understanding) 2017.
Okinawa, Japa
Sequence Teacher-Student Training of Acoustic Models for Automatic Free Speaking Language Assessment
A high performance automatic speech recognition (ASR) system is
an important constituent component of an automatic language assessment system for free speaking language tests. The ASR system
is required to be capable of recognising non-native spontaneous English
speech and to be deployable under real-time conditions. The
performance of ASR systems can often be significantly improved by
leveraging upon multiple systems that are complementary, such as an
ensemble. Ensemble methods, however, can be computationally expensive,
often requiring multiple decoding runs, which makes them
impractical for deployment. In this paper, a lattice-free implementation
of sequence-level teacher-student training is used to reduce this
computational cost, thereby allowing for real-time applications. This
method allows a single student model to emulate the performance of
an ensemble of teachers, but without the need for multiple decoding
runs. Adaptations of the student model to speakers from different
first languages (L1s) and grades are also explored.Cambridge Assessment Englis
Survey of deep representation learning for speech emotion recognition
Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual eort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated \textit{deep representation learning} where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER
Achieving Multi-Accent ASR via Unsupervised Acoustic Model Adaptation
International audienceCurrent automatic speech recognition (ASR) systems trained on native speech often perform poorly when applied to non-native or accented speech. In this work, we propose to compute x-vector-like accent embeddings and use them as auxiliary inputs to an acoustic model trained on native data only in order to improve the recognition of multi-accent data comprising native, non-native, and accented speech. In addition, we leverage untranscribed accented training data by means of semi-supervised learning. Our experiments show that acoustic models trained with the proposed accent embeddings outperform those trained with conventional i-vector or x-vector speaker embeddings, and achieve a 15% relative word error rate (WER) reduction on non-native and accented speech w.r.t. acoustic models trained with regular spectral features only. Semi-supervised training using just 1 hour of untranscribed speech per accent yields an additional 15% relative WER reduction w.r.t. models trained on native data only
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