Word boundary detection

Robust word boundary detection is essential for the efficient and accurate performance of an automatic speech recognition system. Although word boundary detection can achieve high accuracy in the presence of stationary noise with high values of SNR, its implementation becomes non-trivial in the presence of non-stationary noise and low SNR values. The purpose of this thesis is to compare and contrast the accuracy and robustness of various word boundary detection techniques and to introduce modifications to better their performance

Adaptive V/UV Speech Detection Based on Characterization of Background Noise

The paper presents an adaptive system for Voiced/Unvoiced (V/UV) speech detection in the presence of background noise. Genetic algorithms were used to select the features that offer the best V/UV detection according to the output of a background Noise Classifier (NC) and a Signal-to-Noise Ratio Estimation (SNRE) system. The system was implemented, and the tests performed using the TIMIT speech corpus and its phonetic classification. The results were compared with a nonadaptive classification system and the V/UV detectors adopted by two important speech coding standards: the V/UV detection system in the ETSI ES 202 212 v1.1.2 and the speech classification in the Selectable Mode Vocoder (SMV) algorithm. In all cases the proposed adaptive V/UV classifier outperforms the traditional solutions giving an improvement of 25% in very noisy environments

Robust Speech Features based on synchrony spectrum determination using PLLs

In this work we propose to include synchrony effects, known to exist in the auditory system, to represent speech signal information in a robust way. The system decomposes the signal in a number of simpler signals, and utilizes a bank of Phase Locked Loops (PLLs) to obtain information of the frequencies present at each time. This information is interpolated in order to obtain a spectral-like representation based in synchrony effects, measured by the PLLs. Noisy speech recognition experiments are performed using this synchrony-based spectrum, which is transformed into a small set of coefficients by using a similar transformation as the one utilized for the Mel cepstrum features. We show their recognition performance compared to Mel cepstrum features obtained from the standard power spectrum. Some recognition improvements are obtained for the case of vocalic sounds for this approach, especially in the case of severe noise conditions.Sociedad Argentina de Informática e Investigación Operativ

Speech Detection Using Gammatone Features And One-class Support Vector Machine

A network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD’s rely on time-domain features and simple thresholds for efficient speech detection however this doesn’t say much about the signal being passed. More sophisticated methods employ machine learning algorithms, but train on specific noises intended for a target environment. Validating speech under a variety of unknown conditions must be possible; as well as differentiating between speech and nonspeech data embedded within the packets. A real-time speech detection method is proposed that relies only on a clean speech model for detection. Through the use of Gammatone filter bank processing, the Cepstrum and several frequency domain features are used to train a One-Class Support Vector Machine which provides a clean-speech model irrespective of environmental noise. A Wiener filter is used to provide improved operation for harsh noise environments. Greater than 90% detection accuracy is achieved for clean speech with approximately 70% accuracy for SNR as low as 5d

New time-frequency domain pitch estimation methods for speed signals under low levels of SNR

The major objective of this research is to develop novel pitch estimation methods capable of handling speech signals in practical situations where only noise-corrupted speech observations are available. With this objective in mind, the estimation task is carried out in two different approaches. In the first approach, the noisy speech observations are directly employed to develop two new time-frequency domain pitch estimation methods. These methods are based on extracting a pitch-harmonic and finding the corresponding harmonic number required for pitch estimation. Considering that voiced speech is the output of a vocal tract system driven by a sequence of pulses separated by the pitch period, in the second approach, instead of using the noisy speech directly for pitch estimation, an excitation-like signal (ELS) is first generated from the noisy speech or its noise- reduced version. In the first approach, at first, a harmonic cosine autocorrelation (HCAC) model of clean speech in terms of its pitch-harmonics is introduced. In order to extract a pitch-harmonic, we propose an optimization technique based on least-squares fitting of the autocorrelation function (ACF) of the noisy speech to the HCAC model. By exploiting the extracted pitch-harmonic along with the fast Fourier transform (FFT) based power spectrum of noisy speech, we then deduce a harmonic measure and a harmonic-to-noise-power ratio (HNPR) to determine the desired harmonic number of the extracted pitch-harmonic. In the proposed optimization, an initial estimate of the pitch-harmonic is obtained from the maximum peak of the smoothed FFT power spectrum. In addition to the HCAC model, where the cross-product terms of different harmonics are neglected, we derive a compact yet accurate harmonic sinusoidal autocorrelation (HSAC) model for clean speech signal. The new HSAC model is then used in the least-squares model-fitting optimization technique to extract a pitch-harmonic. In the second approach, first, we develop a pitch estimation method by using an excitation-like signal (ELS) generated from the noisy speech. To this end, a technique is based on the principle of homomorphic deconvolution is proposed for extracting the vocal-tract system (VTS) parameters from the noisy speech, which are utilized to perform an inverse-filtering of the noisy speech to produce a residual signal (RS). In order to reduce the effect of noise on the RS, a noise-compensation scheme is introduced in the autocorrelation domain. The noise-compensated ACF of the RS is then employed to generate a squared Hilbert envelope (SHE) as the ELS of the voiced speech. With a view to further overcome the adverse effect of noise on the ELS, a new symmetric normalized magnitude difference function of the ELS is proposed for eventual pitch estimation. Cepstrum has been widely used in speech signal processing but has limited capability of handling noise. One potential solution could be the introduction of a noise reduction block prior to pitch estimation based on the conventional cepstrum, a framework already available in many practical applications, such as mobile communication and hearing aids. Motivated by the advantages of the existing framework and considering the superiority of our ELS to the speech itself in providing clues for pitch information, we develop a cepstrum-based pitch estimation method by using the ELS obtained from the noise-reduced speech. For this purpose, we propose a noise subtraction scheme in frequency domain, which takes into account the possible cross-correlation between speech and noise and has advantages of noise being updated with time and adjusted at each frame. The enhanced speech thus obtained is utilized to extract the vocal-tract system (VTS) parameters via the homomorphic deconvolution technique. A residual signal (RS) is then produced by inverse-filtering the enhanced speech with the extracted VTS parameters. It is found that, unlike the previous ELS-based method, the squared Hilbert envelope (SHE) computed from the RS of the enhanced speech without noise compensation, is sufficient to represent an ELS. Finally, in order to tackle the undesirable effect of noise of the ELS at a very low SNR and overcome the limitation of the conventional cepstrum in handling different types of noises, a time-frequency domain pseudo cepstrum of the ELS of the enhanced speech, incorporating information of both magnitude and phase spectra of the ELS, is proposed for pitch estimation. (Abstract shortened by UMI.

A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring

In recent years, the field of computational sustainability has striven to apply artificial intelligence techniques to solve ecological and environmental problems. In ecology, a key issue for the safeguarding of our planet is the monitoring of biodiversity. Automated acoustic recognition of species aims to provide a cost-effective method for biodiversity monitoring. This is particularly appealing for detecting endangered animals with a distinctive call, such as the New Forest cicada. To this end, we pursue a crowdsourcing approach, whereby the millions of visitors to the New Forest, where this insect was historically found, will help to monitor its presence by means of a smartphone app that can detect its mating call. Existing research in the field of acoustic insect detection has typically focused upon the classification of recordings collected from fixed field microphones. Such approaches segment a lengthy audio recording into individual segments of insect activity, which are independently classified using cepstral coefficients extracted from the recording as features. This paper reports on a contrasting approach, whereby we use crowdsourcing to collect recordings via a smartphone app, and present an immediate feedback to the users as to whether an insect has been found. Our classification approach does not remove silent parts of the recording via segmentation, but instead uses the temporal patterns throughout each recording to classify the insects present. We show that our approach can successfully discriminate between the call of the New Forest cicada and similar insects found in the New Forest, and is robust to common types of environment noise. A large scale trial deployment of our smartphone app collected over 6000 reports of insect activity from over 1000 users. Despite the cicada not having been rediscovered in the New Forest, the effectiveness of this approach was confirmed for both the detection algorithm, which successfully identified the same cicada through the app in countries where the same species is still present, and of the crowdsourcing methodology, which collected a vast number of recordings and involved thousands of contributors.</p