Subjective Evaluation of Join Cost Functions used in Unit Selection Speech Synthesis

In our previous papers, we have proposed join cost functions derived from spectral distances, which have good correlations with perceptual scores obtained for a range of concatenation discontinuities. To further validate their ability to predict concatenation discontinuities, we have chosen the best three spectral distances and evaluated them subjectively in a listening test. The unit sequences for synthesis stimuli are obtained from a state-of-the-art unit selection text-to-speech system: rVoice from Rhetorical Systems Ltd. In this paper, we report listeners’ preferences for each of the three join cost functions

Direct optimisation of a multilayer perceptron for the estimation of cepstral mean and variance statistics

We propose an alternative means of training a multilayer perceptron for the task of speech activity detection based on a criterion to minimise the error in the estimation of mean and variance statistics for speech cepstrum based features using the Kullback-Leibler divergence. We present our baseline and proposed speech activity detection approaches for multi-channel meeting room recordings and demonstrate the effectiveness of the new criterion by comparing the two approaches when used to carry out cepstrum mean and variance normalisation of features used in our meeting ASR system

Subjective Evaluation of Join Cost and Smoothing Methods for Unit Selection Speech Synthesis

In unit selection-based concatenative speech synthesis, join cost (also known as concatenation cost), which measures how well two units can be joined together, is one of the main criteria for selecting appropriate units from the inventory. Usually, some form of local parameter smoothing is also needed to disguise the remaining discontinuities. This paper presents a subjective evaluation of three join cost functions and three smoothing methods. We also describe the design and performance of a listening test. The three join cost functions were taken from our previous study, where we proposed join cost functions derived from spectral distances, which have good correlations with perceptual scores obtained for a range of concatenation discontinuities. This evaluation allows us to further validate their ability to predict concatenation discontinuities. The units for synthesis stimuli are obtained from a state-of-the-art unit selection text-to-speech system: rVoice from Rhetorical Systems Ltd. In this paper, we report listeners' preferences for each join cost in combination with each smoothing method

Subjective Evaluation of Join Cost Functions Used in Unit Selection Speech Synthesis

In our previous papers, we have proposed join cost functions derived from spectral distances, which have good correlations with perceptual scores obtained for a range of concatenation discontinuities. To further validate their ability to predict concatenation discontinuities, we have chosen the best three spectral distances and evaluated them subjectively in a listening test. The unit sequences for synthesis stimuli are obtained from a state-of-the-art unit selection text-to-speech system: rVoice from Rhetorical Systems Ltd. In this paper, we report listeners' preferences for each of the three join cost functions

Using Posterior-Based Features in Template Matching for Speech Recognition

Given the availability of large speech corpora, as well as the increasing of memory and computational resources, the use of template matching approaches for automatic speech recognition (ASR) have recently attracted new attention. In such template-based approaches, speech is typically represented in terms of acoustic vector sequences, using spectral-based features such as MFCC of PLP, and local distances are usually based on Euclidean or Mahalanobis distances. In the present paper, we further investigate template-based ASR and show (on a continuous digit recognition task) that the use of posterior-based features significantly improves the standard template-based approaches, yielding to systems that are very competitive to state-of-the-art HMMs, even when using a very limited number (e.g., 10) of reference templates. Since those posteriors-based features can also be interpreted as a probability distribution, we also show that using Kullback-Leibler (KL) divergence as a local distance further improves the performance of the template-based approach, now beating state-of-the-art of more complex posterior-based HMMs systems (usually referred to as "Tandem")

The segmentation of multi-channel meeting recordings for automatic speech recognition

One major research challenge in the domain of the analysis of meeting room data is the automatic transcription of what is spoken during meetings, a task which has gained considerable attention within the ASR research community through the NIST rich transcription evaluations conducted over the last three years. One of the major difficulties in carrying out automatic speech recognition (ASR) on this data is dealing with the challenging recording environment, which has instigated the development of novel audio pre-processing approaches. In this paper we present a system for the automatic segmentation of multiple-channel individual headset microphone (IHM) meeting recordings for automatic speech recognition. The system relies on an MLP classifier trained from several meeting room corpora to identify speech/non-speech segments of the recordings. We give a detailed analysis of the segmentation performance for a number of system configurations, with our best system achieving ASR performance on automatically generated segments within 1.3\% (3.7\% relative) of a manual segmentation of the data

An Acoustic Model Based on Kullback-Leibler Divergence for Posterior Features

This paper investigates the use of features based on posterior probabilities of subword units such as phonemes. These features are typically transformed when used as inputs for a hidden Markov model with mixture of Gaussians as emission distribution (HMM/GMM). In this work, we introduce a novel acoustic model that avoids the Gaussian assumption and directly uses posterior features without any transformation. This model is described by a finite state machine where each state is characterized by a target distribution and the cost function associated to each state is given by the Kullback-Leibler (KL) divergence between its target distribution and the posterior features. Furthermore, hybrid HMM/ANN system can be seen as a particular case of this KL-based model where state target distributions are predefined. A training method is also presented that minimizes the KL-divergence between the state target distributions and the posteriors features

Using Pitch as Prior Knowledge in Template-Based Speech Recognition

In a previous paper on speech recognition, we showed that templates can better capture the dynamics of speech signal compared to parametric models such as hidden Markov models. The key point in template matching approaches is finding the most similar templates to the test utterance. Traditionally, this selection is given by a distortion measure on the acoustic features. In this work, we propose to improve this template selection with the use of meta-linguistic information as prior knowledge. In this way, similarity is not only based on acoustic features but also on other sources of information that are present in the speech signal. Results on a continuous digit recognition task confirm the statement that similarity between words does not only depend on acoustic features since we obtained 24\% relative improvement over the baseline. Interestingly, results are better even when compared to a system with no prior information but a larger number of templates