Voice Activity Detection Method Research Articles

Robust speech recognition for the control of wheelchairs by inarticulate speech of the severely disabled

The goal of this research is to develop voice‐controlled wheelchairs that can be operated by inarticulate speech affected by severe cerebral palsy or quadriplegia, for instance. In this case, principal factors obstructing recognition performance are significant pronunciation variation caused by difficulty in stable articulation and bad influences of variety of noise in the real environment. To cope with the pronunciation variations, pronunciation lexicons that consist of multitemplates of reference patterns are utilized. The reference patterns are represented with subphonetic codes, which can describe variations of inarticulate speech more precisely than ordinary phonetic transcriptions. Pronunciation lexicons are generated by generalizing coded samples into compact representation of templates based on DP and data mining. For noise robustness, a voice activity detection method is investigated in order to circumvent friction of microphone, cough, etc. A sound source localization using a microphone array is also integrated in order to reject sounds from outside of the wheelchair. These methods are integrated into a system that can be mounted on a wheelchair. A usability test operated by a severe cerebral palsy patient in the real environment results in 95.8% accuracy within 1404 samples for the recognition of the five‐command set. [Work supported by MEXT.]

Hard C-means clustering for voice activity detection

An effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The proposed speech/pause discrimination method is based on a hard-decision clustering approach built on a set of subband log-energies and noise prototypes that define a cluster. Detecting the presence of speech (a new cluster) is achieved using a basic sequential algorithm scheme (BSAS) according to a given “distance” (in this case, geometrical distance) and a suitable threshold. The accuracy of the Cluster VAD (ClVAD) algorithm lies in the use of a decision function defined over a multiple-observation (MO) window of averaged subband log-energies and a suitable noise subspace model defined in terms of prototypes. In addition, the reduced computational cost of the clustering approach makes it adequate for real-time applications, i.e. speech recognition. An exhaustive analysis is conducted on the Spanish SpeechDat-Car databases in order to assess the performance of the proposed method and to compare it to existing standard VAD methods. The results show improvements in detection accuracy over standard VADs such as ITU-T G.729, ETSI GSM AMR and ETSI AFE and a representative set of recently reported VAD algorithms for noise robust speech processing.

An effective cluster-based model for robust speech detection and speech recognition in noisy environments

This paper shows an accurate speech detection algorithm for improving the performance of speech recognition systems working in noisy environments. The proposed method is based on a hard decision clustering approach where a set of prototypes is used to characterize the noisy channel. Detecting the presence of speech is enabled by a decision rule formulated in terms of an averaged distance between the observation vector and a cluster-based noise model. The algorithm benefits from using contextual information, a strategy that considers not only a single speech frame but also a neighborhood of data in order to smooth the decision function and improve speech detection robustness. The proposed scheme exhibits reduced computational cost making it adequate for real time applications, i.e., automated speech recognition systems. An exhaustive analysis is conducted on the AURORA 2 and AURORA 3 databases in order to assess the performance of the algorithm and to compare it to existing standard voice activity detection (VAD) methods. The results show significant improvements in detection accuracy and speech recognition rate over standard VADs such as ITU-T G.729, ETSI GSM AMR, and ETSI AFE for distributed speech recognition and a representative set of recently reported VAD algorithms.

Statistical Voice Activity Detection Using Low-Variance Spectrum Estimation and an Adaptive Threshold

Traditionally, voice activity detection algorithms are based on any combination of general speech properties such as temporal energy variations, periodicity, and spectrum. This paper describes a novel statistical method for voice activity detection using a signal-to-noise ratio measure. The method employs a low-variance spectrum estimate and determines an optimal threshold based on the estimated noise statistics. A possible implementation is presented and evaluated over a large test set and compared to current modern standardized algorithms. The evaluations indicate promising results with the proposed scheme being comparable or favorable over the whole test set.

TESTING FOR INDEPENDENCE AND LRT FOR VAD

In this paper we propose a simple method for Voice Activity Detection (VAD) in noisy environments based on periodogram of squares. The approach is based on a test for independence between a sequence of identically distributed random variables using the classical technique of Likelihood Ratio Test (LRT). This algorithm differs from many others in the way the decision rule is formulated (detection tests) and the domain used in this approach. Clear improvements in the speed of speech/non-speech discrimination accuracy demonstrate the effectiveness of the proposed VAD. It has been shown that application of statistical detection test leads to a better separation of the speech and noise distributions, thus allowing a more effective discrimination and a tradeoff between complexity and performance. The experimental analysis carried out on the AURORA databases and tasks provides an extensive performance evaluation together with an exhaustive comparison to the standard VADs such as ITU G.729, GSM AMR and ETSI AFE for distributed speech recognition (DSR), and other recently reported VADs based in statistical tests.

Model-based voice activity detection system and method using a log-likelihood ratio and pitch

A system and method for voice activity detection, in accordance with the invention includes the steps of inputting data including frames of speech and noise, and deciding if the frames of the input data include speech or noise by employing a log-likelihood ratio test statistic and pitch. The frames of the input data are tagged based on the log-likelihood ratio test statistic and pitch characteristics of the input data as being most likely noise or most likely speech. The tags are counted in a plurality of frames to determine if the input data is speech or noise.

Voice activity detection system and method

An improved voice activity detection system and method is provided for use in speakerphones and other voice activated systems. To facilitate switching between various operating modes, the voice activity detection scheme utilizes a new voice energy term which is based on an integral of the absolute value of a derivative of a speech signal. Voice activity is detected during a silence mode by comparing a first ratio of a current voice energy value to a background noise value with a voice activity threshold value. Voice activity is detected when the first ratio is greater than the voice activity threshold value. Another step involves identifying a direction of the voice activity during a transmit and receive mode by comparing a second ratio of a transmit path voice energy value to a receive path voice energy value with a transmit threshold value and a receive threshold value. When the second ratio is greater than the transmit threshold value, voice activity is present in the transmit path. Similarly, when the second ratio is less than the receive threshold value, then voice activity is present in the receive path. Following the detection of voice activity in one of the paths, the speakerphone or voice activated system begins transitioning to the applicable mode by gradually suppressing the signal in the other path according to the value of the second ratio.

Voice activity detection in nonstationary noise

A new fusion method for voice activity detection in additive nonstationary noise is suggested. A performance study of the methods: fusion, the geometrically adaptive energy level, periodicity measure, and zero crossings rates, is presented. The new method is shown to operate reliably down to -5 dB SNR.

Method and device for voice activity detection and a communication device

The invention concerns a voice activity detection device in which an input speech signal (x(n)) is divided in subsignals (S(s)) representing specific frequency bands and noise (N(s)) is estimated in the subsignals. On basis of the estimated noise in the subsignals, subdecision signals (SNR(s)) are generated and a voice activity decision (Vind) for the input speech signal is formed on basis of the subdecision signals. Spectrum components of the input speech signal and a noise estimate are calculated and compared. More specifically a signal-to-noise ratio is calculated for each subsignal and each signal-to-noise ratio represents a subdecision signal (SNR(s)). From the signal-to-noise ratios a value proportional to their sum is calculated and compared with a threshold value and a voice activity decision signal (Vind) for the input speech signal is formed on basis of the comparison.

Voice activity detection method and apparatus using the same

Voice activity detection method and apparatus using the same