Robust Speech Detection for Noisy Environments

This paper presents a robust voice activity detector (VAD) based on hidden Markov models (HMM) to improve speech recognition systems in stationary and non-stationary noise environments: inside motor vehicles (like cars or planes) or inside buildings close to high traffic places (like in a control tower for air traffic control (ATC)). In these environments, there is a high stationary noise level caused by vehicle motors and additionally, there could be people speaking at certain distance from the main speaker producing non-stationary noise. The VAD presented in this paper is characterized by a new front-end and a noise level adaptation process that increases significantly the VAD robustness for different signal to noise ratios (SNRs). The feature vector used by the VAD includes the most relevant Mel Frequency Cepstral Coefficients (MFCC), normalized log energy and delta log energy. The proposed VAD has been evaluated and compared to other well-known VADs using three databases containing different noise conditions: speech in clean environments (SNRs mayor que 20 dB), speech recorded in stationary noise environments (inside or close to motor vehicles), and finally, speech in non stationary environments (including noise from bars, television and far-field speakers). In the three cases, the detection error obtained with the proposed VAD is the lowest for all SNRs compared to Acero¿s VAD (reference of this work) and other well-known VADs like AMR, AURORA or G729 annex b

Improvement of Text Dependent Speaker Identification System Using Neuro-Genetic Hybrid Algorithm in Office Environmental Conditions

In this paper, an improved strategy for automated text dependent speaker identification system has been proposed in noisy environment. The identification process incorporates the Neuro-Genetic hybrid algorithm with cepstral based features. To remove the background noise from the source utterance, wiener filter has been used. Different speech pre-processing techniques such as start-end point detection algorithm, pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. RCC, MFCC, ?MFCC, ??MFCC, LPC and LPCC have been used to extract the features. After feature extraction of the speech, Neuro-Genetic hybrid algorithm has been used in the learning and identification purposes. Features are extracted by using different techniques to optimize the performance of the identification. According to the VALID speech database, the highest speaker identification rate of 100.000% for studio environment and 82.33% for office environmental conditions have been achieved in the close set text dependent speaker identification system

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Real-time noise-robust speech detection

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 87-89).As part of the development of an autonomous forklift of the Agile Robotics Lab at MIT's Computer Science and Artificial Intelligence Lab (CSAIL), this thesis explores the effectiveness and application of various noise-robust techniques towards real-time speech detection in real environments. Dynamic noises in the environment (including motor noise, babble noise, and other noises in a warehouse setting) can dramatically alter the speech signal, making speech detection much more difficult. In addition to the noise environments, another issue is the urgent nature of the situation, leading to the production of shouted speech. Given these constraints, the forklift must be highly accurate in detecting speech at all times, since safety is a major concern in our application. This thesis analyzes different speech properties that would be useful in distinguishing speech from noise in various noise environments. We look at various features in an effort to optimize the overall shout detection system. In addition to identifying speech features, this thesis also uses common signal processing techniques to enhance the speech signals in audio waveforms. In addition to the optimal speech features and speech enhancement techniques, we present a shout detection algorithm that is optimized towards the application of the autonomous forklift. We measure the performance of the resulting system by comparing it to other baseline systems and show 38% improvement over a baseline task.by Kevin Y. Luu.M.Eng