234 research outputs found
Auditory processing-based features for improving speech recognition in adverse acoustic conditions
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Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
The rapid population aging has stimulated the development of assistive
devices that provide personalized medical support to the needies suffering from
various etiologies. One prominent clinical application is a computer-assisted
speech training system which enables personalized speech therapy to patients
impaired by communicative disorders in the patient's home environment. Such a
system relies on the robust automatic speech recognition (ASR) technology to be
able to provide accurate articulation feedback. With the long-term aim of
developing off-the-shelf ASR systems that can be incorporated in clinical
context without prior speaker information, we compare the ASR performance of
speaker-independent bottleneck and articulatory features on dysarthric speech
used in conjunction with dedicated neural network-based acoustic models that
have been shown to be robust against spectrotemporal deviations. We report ASR
performance of these systems on two dysarthric speech datasets of different
characteristics to quantify the achieved performance gains. Despite the
remaining performance gap between the dysarthric and normal speech, significant
improvements have been reported on both datasets using speaker-independent ASR
architectures.Comment: to appear in Computer Speech & Language -
https://doi.org/10.1016/j.csl.2019.05.002 - arXiv admin note: substantial
text overlap with arXiv:1807.1094
Speaker Identification and Spoken word Recognition in Noisy Environment using Different Techniques
In this work, an attempt is made to design ASR systems through software/computer programs which would perform Speaker Identification, Spoken word recognition and combination of both speaker identification and Spoken word recognition in general noisy environment. Automatic Speech Recognition system is designed for Limited vocabulary of Telugu language words/control commands. The experiments are conducted to find the better combination of feature extraction technique and classifier model that will perform well in general noisy environment (Home/Office environment where noise is around 15-35 dB). A recently proposed features extraction technique Gammatone frequency coefficients which is reported as the best fit to the human auditory system is chosen for the experiments along with the more common feature extraction techniques MFCC and PLP as part of Front end process (i.e. speech features extraction). Two different Artificial Neural Network classifiers Learning Vector Quantization (LVQ) neural networks and Radial Basis Function (RBF) neural networks along with Hidden Markov Models (HMMs) are chosen for the experiments as part of Back end process (i.e. training/modeling the ASRs). The performance of different ASR systems that are designed by utilizing the 9 different combinations (3 feature extraction techniques and 3 classifier models) are analyzed in terms of spoken word recognition and speaker identification accuracy success rate, design time of ASRs, and recognition / identification response time .The testing speech samples are recorded in general noisy conditions i.e.in the existence of air conditioning noise, fan noise, computer key board noise and far away cross talk noise. ASR systems designed and analyzed programmatically in MATLAB 2013(a) Environment
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation
We present state-of-the-art automatic speech recognition (ASR) systems
employing a standard hybrid DNN/HMM architecture compared to an attention-based
encoder-decoder design for the LibriSpeech task. Detailed descriptions of the
system development, including model design, pretraining schemes, training
schedules, and optimization approaches are provided for both system
architectures. Both hybrid DNN/HMM and attention-based systems employ
bi-directional LSTMs for acoustic modeling/encoding. For language modeling, we
employ both LSTM and Transformer based architectures. All our systems are built
using RWTHs open-source toolkits RASR and RETURNN. To the best knowledge of the
authors, the results obtained when training on the full LibriSpeech training
set, are the best published currently, both for the hybrid DNN/HMM and the
attention-based systems. Our single hybrid system even outperforms previous
results obtained from combining eight single systems. Our comparison shows that
on the LibriSpeech 960h task, the hybrid DNN/HMM system outperforms the
attention-based system by 15% relative on the clean and 40% relative on the
other test sets in terms of word error rate. Moreover, experiments on a reduced
100h-subset of the LibriSpeech training corpus even show a more pronounced
margin between the hybrid DNN/HMM and attention-based architectures.Comment: Proceedings of INTERSPEECH 201
Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes
This paper is about alerting acoustic event detection and sound source
localisation in an urban scenario. Specifically, we are interested in spotting
the presence of horns, and sirens of emergency vehicles. In order to obtain a
reliable system able to operate robustly despite the presence of traffic noise,
which can be copious, unstructured and unpredictable, we propose to treat the
spectrograms of incoming stereo signals as images, and apply semantic
segmentation, based on a Unet architecture, to extract the target sound from
the background noise. In a multi-task learning scheme, together with signal
denoising, we perform acoustic event classification to identify the nature of
the alerting sound. Lastly, we use the denoised signals to localise the
acoustic source on the horizon plane, by regressing the direction of arrival of
the sound through a CNN architecture. Our experimental evaluation shows an
average classification rate of 94%, and a median absolute error on the
localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of
2.5{\deg} when operating on frames of 2.5s. The system offers excellent
performance in particularly challenging scenarios, where the noise level is
remarkably high.Comment: 6 pages, 9 figure
Combining Spectral Representations for Large Vocabulary Continuous Speech Recognition
In this paper we investigate the combination of complementary acoustic feature streams in large vocabulary continuous speech recognition (LVCSR). We have explored the use of acoustic features obtained using a pitch-synchronous analysis, STRAIGHT, in combination with conventional features such as mel frequency cepstral coefficients. Pitch-synchronous acoustic features are of particular interest when used with vocal tract length normalisation (VTLN) which is known to be affected by the fundamental frequency. We have combined these spectral representations directly at the acoustic feature level using heteroscedastic linear discriminant analysis (HLDA) and at the system level using ROVER. We evaluated this approach on three LVCSR tasks: dictated newspaper text (WSJCAM0), conversational telephone speech (CTS), and multiparty meeting transcription. The CTS and meeting transcription experiments were both evaluated using standard NIST test sets and evaluation protocols. Our results indicate that combining conventional and pitch-synchronous acoustic feature sets using HLDA results in a consistent, significant decrease in word error rate across all three tasks. Combining at the system level using ROVER resulted in a further significant decrease in word error rate
Idealized computational models for auditory receptive fields
This paper presents a theory by which idealized models of auditory receptive
fields can be derived in a principled axiomatic manner, from a set of
structural properties to enable invariance of receptive field responses under
natural sound transformations and ensure internal consistency between
spectro-temporal receptive fields at different temporal and spectral scales.
For defining a time-frequency transformation of a purely temporal sound
signal, it is shown that the framework allows for a new way of deriving the
Gabor and Gammatone filters as well as a novel family of generalized Gammatone
filters, with additional degrees of freedom to obtain different trade-offs
between the spectral selectivity and the temporal delay of time-causal temporal
window functions.
When applied to the definition of a second-layer of receptive fields from a
spectrogram, it is shown that the framework leads to two canonical families of
spectro-temporal receptive fields, in terms of spectro-temporal derivatives of
either spectro-temporal Gaussian kernels for non-causal time or the combination
of a time-causal generalized Gammatone filter over the temporal domain and a
Gaussian filter over the logspectral domain. For each filter family, the
spectro-temporal receptive fields can be either separable over the
time-frequency domain or be adapted to local glissando transformations that
represent variations in logarithmic frequencies over time. Within each domain
of either non-causal or time-causal time, these receptive field families are
derived by uniqueness from the assumptions.
It is demonstrated how the presented framework allows for computation of
basic auditory features for audio processing and that it leads to predictions
about auditory receptive fields with good qualitative similarity to biological
receptive fields measured in the inferior colliculus (ICC) and primary auditory
cortex (A1) of mammals.Comment: 55 pages, 22 figures, 3 table
Environmentally robust ASR front-end for deep neural network acoustic models
This paper examines the individual and combined impacts of various front-end approaches on the performance of deep neural network (DNN) based speech recognition systems in distant talking situations, where acoustic environmental distortion degrades the recognition performance. Training of a DNN-based acoustic model consists of generation of state alignments followed by learning the network parameters. This paper first shows that the network parameters are more sensitive to the speech quality than the alignments and thus this stage requires improvement. Then, various front-end robustness approaches to addressing this problem are categorised based on functionality. The degree to which each class of approaches impacts the performance of DNN-based acoustic models is examined experimentally. Based on the results, a front-end processing pipeline is proposed for efficiently combining different classes of approaches. Using this front-end, the combined effects of different classes of approaches are further evaluated in a single distant microphone-based meeting transcription task with both speaker independent (SI) and speaker adaptive training (SAT) set-ups. By combining multiple speech enhancement results, multiple types of features, and feature transformation, the front-end shows relative performance gains of 7.24% and 9.83% in the SI and SAT scenarios, respectively, over competitive DNN-based systems using log mel-filter bank features.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.csl.2014.11.00
Comparative Analysis of the wav2vec 2.0 Feature Extractor
Automatic speech recognition (ASR) systems typically use handcrafted feature
extraction pipelines. To avoid their inherent information loss and to achieve
more consistent modeling from speech to transcribed text, neural raw waveform
feature extractors (FEs) are an appealing approach. Also the wav2vec 2.0 model,
which has recently gained large popularity, uses a convolutional FE which
operates directly on the speech waveform. However, it is not yet studied
extensively in the literature. In this work, we study its capability to replace
the standard feature extraction methods in a connectionist temporal
classification (CTC) ASR model and compare it to an alternative neural FE. We
show that both are competitive with traditional FEs on the LibriSpeech
benchmark and analyze the effect of the individual components. Furthermore, we
analyze the learned filters and show that the most important information for
the ASR system is obtained by a set of bandpass filters.Comment: Accepted at ITG 202
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