257,128 research outputs found
English Broadcast News Speech Recognition by Humans and Machines
With recent advances in deep learning, considerable attention has been given
to achieving automatic speech recognition performance close to human
performance on tasks like conversational telephone speech (CTS) recognition. In
this paper we evaluate the usefulness of these proposed techniques on broadcast
news (BN), a similar challenging task. We also perform a set of recognition
measurements to understand how close the achieved automatic speech recognition
results are to human performance on this task. On two publicly available BN
test sets, DEV04F and RT04, our speech recognition system using LSTM and
residual network based acoustic models with a combination of n-gram and neural
network language models performs at 6.5% and 5.9% word error rate. By achieving
new performance milestones on these test sets, our experiments show that
techniques developed on other related tasks, like CTS, can be transferred to
achieve similar performance. In contrast, the best measured human recognition
performance on these test sets is much lower, at 3.6% and 2.8% respectively,
indicating that there is still room for new techniques and improvements in this
space, to reach human performance levels.Comment: \copyright 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
future media, including reprinting/republishing this material for advertising
or promotional purposes, creating new collective works, for resale or
redistribution to servers or lists, or reuse of any copyrighted component of
this work in other work
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation
Data augmentation is one of the most effective ways to make end-to-end
automatic speech recognition (ASR) perform close to the conventional hybrid
approach, especially when dealing with low-resource tasks. Using recent
advances in speech synthesis (text-to-speech, or TTS), we build our TTS system
on an ASR training database and then extend the data with synthesized speech to
train a recognition model. We argue that, when the training data amount is
relatively low, this approach can allow an end-to-end model to reach hybrid
systems' quality. For an artificial low-to-medium-resource setup, we compare
the proposed augmentation with the semi-supervised learning technique. We also
investigate the influence of vocoder usage on final ASR performance by
comparing Griffin-Lim algorithm with our modified LPCNet. When applied with an
external language model, our approach outperforms a semi-supervised setup for
LibriSpeech test-clean and only 33% worse than a comparable supervised setup.
Our system establishes a competitive result for end-to-end ASR trained on
LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for
test-other
Robust Sound Event Classification using Deep Neural Networks
The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise.
This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques
Current trends in multilingual speech processing
In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processin
Digital signal processing algorithms for automatic voice recognition
The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms
Visual Speech Recognition
Lip reading is used to understand or interpret speech without hearing it, a
technique especially mastered by people with hearing difficulties. The ability
to lip read enables a person with a hearing impairment to communicate with
others and to engage in social activities, which otherwise would be difficult.
Recent advances in the fields of computer vision, pattern recognition, and
signal processing has led to a growing interest in automating this challenging
task of lip reading. Indeed, automating the human ability to lip read, a
process referred to as visual speech recognition (VSR) (or sometimes speech
reading), could open the door for other novel related applications. VSR has
received a great deal of attention in the last decade for its potential use in
applications such as human-computer interaction (HCI), audio-visual speech
recognition (AVSR), speaker recognition, talking heads, sign language
recognition and video surveillance. Its main aim is to recognise spoken word(s)
by using only the visual signal that is produced during speech. Hence, VSR
deals with the visual domain of speech and involves image processing,
artificial intelligence, object detection, pattern recognition, statistical
modelling, etc.Comment: Speech and Language Technologies (Book), Prof. Ivo Ipsic (Ed.), ISBN:
978-953-307-322-4, InTech (2011
- …