Antonio J. Rubio

Histogram equalization of speech representation for robust speech recognition

This paper describes a method of compensating for nonlinear distortions in speech representation caused by noise. The method described here is based on the histogram equalization method often used in digital image processing. Histogram equalization is applied to each component of the feature vector in order to improve the robustness of speech recognition systems. The paper describes how the proposed method can be applied to robust speech recognition and it is compared with other compensation techniques. The recognition experiments, including results in the AURORA II framework, demonstrate the effectiveness of histogram equalization when it is applied either alone or in combination with other compensation techniques.

Efficient voice activity detection algorithms using long-term speech information

Abstract Currently, there are technology barriers inhibiting speech processing systems working under extreme noisy conditions. The emerging applications of speech technology, especially in the fields of wireless communications, digital hearing aids or speech recognition, are examples of such systems and often require a noise reduction technique operating in combination with a precise voice activity detector (VAD). This paper presents a new VAD algorithm for improving speech detection robustness in noisy environments and the performance of speech recognition systems. The algorithm measures the long-term spectral divergence (LTSD) between speech and noise and formulates the speech/non-speech decision rule by comparing the long-term spectral envelope to the average noise spectrum, thus yielding a high discriminating decision rule and minimizing the average number of decision errors. The decision threshold is adapted to the measured noise energy while a controlled hang-over is activated only when the observed signal-to-noise ratio is low. It is shown by conducting an analysis of the speech/non-speech LTSD distributions that using long-term information about speech signals is beneficial for VAD. The proposed algorithm is compared to the most commonly used VADs in the field, in terms of speech/non-speech discrimination and in terms of recognition performance when the VAD is used for an automatic speech recognition system. Experimental results demonstrate a sustained advantage over standard VADs such as G.729 and adaptive multi-rate (AMR) which were used as a reference, and over the VADs of the advanced front-end for distributed speech recognition.

Statistical voice activity detection using a multiple observation likelihood ratio test

Currently, there are technology barriers inhibiting speech processing systems that work in extremely noisy conditions from meeting the demands of modern applications. This letter presents a new voice activity detector (VAD) for improving speech detection robustness in noisy environments and the performance of speech recognition systems. The algorithm defines an optimum likelihood ratio test (LRT) involving multiple and independent observations. The so-defined decision rule reports significant improvements in speech/nonspeech discrimination accuracy over existing VAD methods that are defined on a single observation and need empirically tuned hangover mechanisms. The algorithm has an inherent delay that, for several applications, including robust speech recognition, does not represent a serious implementation obstacle. An analysis of the overlap between the distributions of the decision variable shows the improved robustness of the proposed approach by means of a clear reduction of the classification error as the number of observations is increased. The proposed strategy is also compared to different VAD methods, including the G.729, AMR, and AFE standards, as well as recently reported algorithms showing a sustained advantage in speech/nonspeech detection accuracy and speech recognition performance.

Analysis and solutions for switching noise coupling in mixed-signal ICs

1. Introduction. 2. Substrate noise characteristics and propagation. 3. Substrate coupling modeling. 4. Substrate biasing and noise coupling. 5. Alternative proposals for reducing substrate noise. 6. Experimental measurements performed on a mixed-signal IC. 7. The cause of switching noise. 8. Digital design for low switching noise. 9. Techniques and circuits for reducing switching noise. 10. Conclusions.

An effective subband OSF-based VAD with noise reduction for robust speech recognition

An effective voice activity detection (VAD) algorithm is proposed for improving speech recognition performance in noisy environments. The approach is based on the determination of the speech/nonspeech divergence by means of specialized order statistics filters (OSFs) working on the subband log-energies. This algorithm differs from many others in the way the decision rule is formulated. Instead of making the decision based on the current frame, it uses OSFs on the subband log-energies which significantly reduces the error probability when discriminating speech from nonspeech in a noisy signal. Clear improvements in speech/nonspeech discrimination accuracy demonstrate the effectiveness of the proposed VAD. It is shown that an increase of the OSF order 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 algorithm also incorporates a noise reduction block working in tandem with the VAD and showed to further improve its accuracy. A previous noise reduction block also improves the accuracy in detecting speech and nonspeech. 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.

Non-linear transformations of the feature space for robust Speech Recognition

The noise usually produces a non-linear distortion of the feature space considered for Automatic Speech Recognition. This distortion causes a mismatch between the training and recognition conditions which significantly degrades the performance of speech recognizers. In this contribution we analyze the effect of the additive noise over cepstral based representations and we compare several approaches to compensate this effect. We discuss the importance of the non-linearities introduced by the noise and we propose a method (based on the histogram equalization technique) specifically oriented to the compensation of the non-linear transformation caused by the additive noise. The proposed method has been evaluated using the AURORA-2 database and task. The recognition results show significant improvements with respect to other compensation methods reported in the bibliography and reveals the importance of the non-linear effects of the noise and the utility of the proposed method.

Diagonalizing properties of the discrete cosine transforms

Since its introduction in 1974 by Ahmed et al., the discrete cosine transform (DCT) has become a significant tool in many areas of digital signal processing, especially in signal compression. There exist eight types of discrete cosine transforms (DCTs). We obtain the eight types of DCTs as the complete orthonormal set of eigenvectors generated by a general form of matrices in the same way as the discrete Fourier transform (DFT) can be obtained as the eigenvectors of an arbitrary circulant matrix. These matrices can be decomposed as the sum of a symmetric Toeplitz matrix plus a Hankel or close to Hankel matrix scaled by some constant factors. We also show that all the previously proposed generating matrices for the DCTs are simply particular cases of these general matrix forms. Using these matrices, we obtain, for each DCT, a class of stationary processes verifying certain conditions with respect to which the corresponding DCT has a good asymptotic behavior in the sense that it approaches Karhunen-Loeve transform performance as the block size N tends to infinity. As a particular result, we prove that the eight types of DCTs are asymptotically optimal for all finite-order Markov processes. We finally study the decorrelating power of the DCTs, obtaining expressions that show the decorrelating behavior of each DCT with respect to any stationary processes.

Assessment of dialogue systems by means of a new simulation technique

In recent years, a question of great interest has been the development of tools and techniqnes to facilitate the evaluation of dialogue systems. The latter can be evaluated from various points of view, such as recognition and understanding rates, dialogue naturalness and robustness against recognition errors. Evaluation usually requires compiling a large corpus of words and sentences uttered by users, relevant to the application domain the system is designed for. This paper proposes a new technique that makes it possible to reuse such a corpus for the evaluation and to check the performance of the system when different dialogue strategies are used. The technique is based on the automatic generation of conversations between the dialogue system, together with an additional dialogue system called user simulator that represents the users interaction with the dialogue system. The technique has been applied to evaluate a dialogue system developed in our lab using two different recognition front-ends and two different dialogue strategies to handle user confirmations. The experiments show that the prompt-dependent recognition front-end achieves better results, but that this front-end is appropriate only if users limit their utterances to those related to the current system prompt. The prompt-independent front-end achieves inferior results, but enables front-end users to utter any permitted utterance at any time, irrespective of the system prompt. In consequence, this front-end may allow a more natural and comfortable interaction. The experiments also show that the re-prompting confirmation strategy enhances system performance for both recognition front-ends.

Thermal coupling in integrated circuits: application to thermal testing

The power dissipated by the devices of a circuit can be construed as a signature of the circuits performance and state. Without disturbing the circuit operation, this power consumption can be monitored by temperature measurements of the silicon die surface via built-in differential temperature sensors. In this paper, dynamic and spatial thermal behavioral characterization of VLSI MOS devices is presented using laser thermoreflectance measurements and on-chip differential temperature sensing circuits. A discussion of the application of built-in differential temperature measurements as an IC test strategy is also presented.

Dynamic Surface Temperature Measurements in ICs

Measuring techniques of the die surface temperature in integrated circuits are reported as very appropriate for failure analysis, for thermal characterization, and for testing modern devices. The paper is arranged as a survey of techniques oriented towards measuring the temperature dynamics of the circuit surface and presenting and discussing both the merits and drawbacks of each technique with regard to the accuracy, reliability and efficiency of the measurements. Two methods are presented in detail: laser probing methods, based on interferometry and thermoreflectance, and embedded CMOS circuit sensors. For these techniques, the physical principles, the state of the art in figures of merit and some application examples are presented