Normal-to-Lombard Adaptation of Speech Synthesis Using Long Short-Term Memory Recurrent Neural Networks

In this article, three adaptation methods are compared based on how well they change the speaking style of a neural network based text-to-speech (TTS) voice. The speaking style conversion adopted here is from normal to Lombard speech. The selected adaptation methods are: auxiliary features (AF), learning hidden unit contribution (LHUC), and fine-tuning (FT). Furthermore, four state-of-the-art TTS vocoders are compared in the same context. The evaluated vocoders are: GlottHMM, GlottDNN, STRAIGHT, and pulse model in log-domain (PML). Objective and subjective evaluations were conducted to study the performance of both the adaptation methods and the vocoders. In the subjective evaluations, speaking style similarity and speech intelligibility were assessed. In addition to acoustic model adaptation, phoneme durations were also adapted from normal to Lombard with the FT adaptation method. In objective evaluations and speaking style similarity tests, we found that the FT method outperformed the other two adaptation methods. In speech intelligibility tests, we found that there were no significant differences between vocoders although the PML vocoder showed slightly better performance compared to the three other vocoders.Peer reviewe

Individual and environment-related acoustic-phonetic strategies for communicating in adverse conditions

In many situations it is necessary to produce speech in ‘adverse conditions’: that is, conditions that make speech communication difficult. Research has demonstrated that speaker strategies, as described by a range of acoustic-phonetic measures, can vary both at the individual level and according to the environment, and are argued to facilitate communication. There has been debate as to the environmental specificity of these adaptations, and their effectiveness in overcoming communication difficulty. Furthermore, the manner and extent to which adaptation strategies differ between individuals is not yet well understood. This thesis presents three studies that explore the acoustic-phonetic adaptations of speakers in noisy and degraded communication conditions and their relationship with intelligibility. Study 1 investigated the effects of temporally fluctuating maskers on global acoustic-phonetic measures associated with speech in noise (Lombard speech). The results replicated findings of increased power in the modulation spectrum in Lombard speech, but showed little evidence of adaptation to masker fluctuations via the temporal envelope. Study 2 collected a larger corpus of semi-spontaneous communicative speech in noise and other degradations perturbing specific acoustic dimensions. Speakers showed different adaptations across the environments that were likely suited to overcome noise (steady and temporally fluctuating), restricted spectral and pitch information by a noise-excited vocoder, and a sensorineural hearing loss simulation. Analyses of inter-speaker variation in both studies 1 and 2 showed behaviour was highly variable and some strategy combinations were identified. Study 3 investigated the intelligibility of strategies ‘tailored’ to specific environments and the relationship between intelligibility and speaker acoustics, finding a benefit of tailored speech adaptations and discussing the potential roles of speaker flexibility, adaptation level, and intrinsic intelligibility. The overall results are discussed in relation to models of communication in adverse conditions and a model accounting for individual variability in these conditions is proposed

Methods for speaking style conversion from normal speech to high vocal effort speech

This thesis deals with vocal-effort-focused speaking style conversion (SSC). Specifically, we studied two topics on conversion of normal speech to high vocal effort. The first topic involves the conversion of normal speech to shouted speech. We employed this conversion in a speaker recognition system with vocal effort mismatch between test and enrollment utterances (shouted speech vs. normal speech). The mismatch causes a degradation of the system's speaker identification performance. As solution, we proposed a SSC system that included a novel spectral mapping, used along a statistical mapping technique, to transform the mel-frequency spectral energies of normal speech enrollment utterances towards their counterparts in shouted speech. We evaluated the proposed solution by comparing speaker identification rates for a state-of-the-art i-vector-based speaker recognition system, with and without applying SSC to the enrollment utterances. Our results showed that applying the proposed SSC pre-processing to the enrollment data improves considerably the speaker identification rates. The second topic involves a normal-to-Lombard speech conversion. We proposed a vocoder-based parametric SSC system to perform the conversion. This system first extracts speech features using the vocoder. Next, a mapping technique, robust to data scarcity, maps the features. Finally, the vocoder synthesizes the mapped features into speech. We used two vocoders in the conversion system, for comparison: a glottal vocoder and the widely used STRAIGHT. We assessed the converted speech from the two vocoder cases with two subjective listening tests that measured similarity to Lombard speech and naturalness. The similarity subjective test showed that, for both vocoder cases, our proposed SSC system was able to convert normal speech to Lombard speech. The naturalness subjective test showed that the converted samples using the glottal vocoder were clearly more natural than those obtained with STRAIGHT

Speech Enhancement for Automatic Analysis of Child-Centered Audio Recordings

Analysis of child-centred daylong naturalist audio recordings has become a de-facto research protocol in the scientific study of child language development. The researchers are increasingly using these recordings to understand linguistic environment a child encounters in her routine interactions with the world. These audio recordings are captured by a microphone that a child wears throughout a day. The audio recordings, being naturalistic, contain a lot of unwanted sounds from everyday life which degrades the performance of speech analysis tasks. The purpose of this thesis is to investigate the utility of speech enhancement (SE) algorithms in the automatic analysis of such recordings. To this effect, several classical signal processing and modern machine learning-based SE methods were employed 1) as a denoiser for speech corrupted with additive noise sampled from real-life child-centred daylong recordings and 2) as front-end for downstream speech processing tasks of addressee classification (infant vs. adult-directed speech) and automatic syllable count estimation from the speech. The downstream tasks were conducted on data derived from a set of geographically, culturally, and linguistically diverse child-centred daylong audio recordings. The performance of denoising was evaluated through objective quality metrics (spectral distortion and instrumental intelligibility) and through the downstream task performance. Finally, the objective evaluation results were compared with downstream task performance results to find whether objective metrics can be used as a reasonable proxy to select SE front-end for a downstream task. The results obtained show that a recently proposed Long Short-Term Memory (LSTM)-based progressive learning architecture provides maximum performance gains in the downstream tasks in comparison with the other SE methods and baseline results. Classical signal processing-based SE methods also lead to competitive performance. From the comparison of objective assessment and downstream task performance results, no predictive relationship between task-independent objective metrics and performance of downstream tasks was found

Intelligibility enhancement of synthetic speech in noise

EC Seventh Framework Programme (FP7/2007-2013)Speech technology can facilitate human-machine interaction and create new communication interfaces. Text-To-Speech (TTS) systems provide speech output for dialogue, notification and reading applications as well as personalized voices for people that have lost the use of their own. TTS systems are built to produce synthetic voices that should sound as natural, expressive and intelligible as possible and if necessary be similar to a particular speaker. Although naturalness is an important requirement, providing the correct information in adverse conditions can be crucial to certain applications. Speech that adapts or reacts to different listening conditions can in turn be more expressive and natural. In this work we focus on enhancing the intelligibility of TTS voices in additive noise. For that we adopt the statistical parametric paradigm for TTS in the shape of a hidden Markov model (HMM-) based speech synthesis system that allows for flexible enhancement strategies. Little is known about which human speech production mechanisms actually increase intelligibility in noise and how the choice of mechanism relates to noise type, so we approached the problem from another perspective: using mathematical models for hearing speech in noise. To find which models are better at predicting intelligibility of TTS in noise we performed listening evaluations to collect subjective intelligibility scores which we then compared to the models’ predictions. In these evaluations we observed that modifications performed on the spectral envelope of speech can increase intelligibility significantly, particularly if the strength of the modification depends on the noise and its level. We used these findings to inform the decision of which of the models to use when automatically modifying the spectral envelope of the speech according to the noise. We devised two methods, both involving cepstral coefficient modifications. The first was applied during extraction while training the acoustic models and the other when generating a voice using pre-trained TTS models. The latter has the advantage of being able to address fluctuating noise. To increase intelligibility of synthetic speech at generation time we proposed a method for Mel cepstral coefficient modification based on the glimpse proportion measure, the most promising of the models of speech intelligibility that we evaluated. An extensive series of listening experiments demonstrated that this method brings significant intelligibility gains to TTS voices while not requiring additional recordings of clear or Lombard speech. To further improve intelligibility we combined our method with noise-independent enhancement approaches based on the acoustics of highly intelligible speech. This combined solution was as effective for stationary noise as for the challenging competing speaker scenario, obtaining up to 4dB of equivalent intensity gain. Finally, we proposed an extension to the speech enhancement paradigm to account for not only energetic masking of signals but also for linguistic confusability of words in sentences. We found that word level confusability, a challenging value to predict, can be used as an additional prior to increase intelligibility even for simple enhancement methods like energy reallocation between words. These findings motivate further research into solutions that can tackle the effect of energetic masking on the auditory system as well as on higher levels of processing

Intelligibility model optimisation approaches for speech pre-enhancement

The goal of improving the intelligibility of broadcast speech is being met by a recent new direction in speech enhancement: near-end intelligibility enhancement. In contrast to the conventional speech enhancement approach that processes the corrupted speech at the receiver-side of the communication chain, the near-end intelligibility enhancement approach pre-processes the clean speech at the transmitter-side, i.e. before it is played into the environmental noise. In this work, we describe an optimisation-based approach to near-end intelligibility enhancement using models of speech intelligibility to improve the intelligibility of speech in noise. This thesis first presents a survey of speech intelligibility models and how the adverse acoustic conditions affect the intelligibility of speech. The purpose of this survey is to identify models that we can adopt in the design of the pre-enhancement system. Then, we investigate the strategies humans use to increase speech intelligibility in noise. We then relate human strategies to existing algorithms for near-end intelligibility enhancement. A closed-loop feedback approach to near-end intelligibility enhancement is then introduced. In this framework, speech modifications are guided by a model of intelligibility. For the closed-loop system to work, we develop a simple spectral modification strategy that modifies the first few coefficients of an auditory cepstral representation such as to maximise an intelligibility measure. We experiment with two contrasting measures of objective intelligibility. The first, as a baseline, is an audibility measure named 'glimpse proportion' that is computed as the proportion of the spectro-temporal representation of the speech signal that is free from masking. We then propose a discriminative intelligibility model, building on the principles of missing data speech recognition, to model the likelihood of specific phonetic confusions that may occur when speech is presented in noise. The discriminative intelligibility measure is computed using a statistical model of speech from the speaker that is to be enhanced. Interim results showed that, unlike the glimpse proportion based system, the discriminative based system did not improve intelligibility. We investigated the reason behind that and we found that the discriminative based system was not able to target the phonetic confusion with the fixed spectral shaping. To address that, we introduce a time-varying spectral modification. We also propose to perform the optimisation on a segment-by-segment basis which enables a robust solution against the fluctuating noise. We further combine our system with a noise-independent enhancement technique, i.e. dynamic range compression. We found significant improvement in non-stationary noise condition, but no significant differences to the state-of-the art system (spectral shaping and dynamic range compression) where found in stationary noise condition

Advanced statistical models for the segregation, identification and measurement of coexisting sound sources

Long-term monitoring of acoustical environments is gaining popularity thanks to the relevant amount of scientific and engineering insights that it provides. The increasing interest is due to the constant growth of storage capacity and computational power to process large amounts of data. In this perspective, machine learning (ML) provides a broad family of data-driven statistical techniques to deal with large databases. Nowadays, the conventional praxis of sound level meter measurements limits the global description of a sound scene to an energetic point of view. The equivalent continuous level Leq represents the main metric to define an acoustic environment, indeed. Finer analyses involve the use of statistical levels. However, acoustic percentiles are based on temporal assumptions, which are not always reliable. A statistical approach, based on the study of the occurrences of sound pressure levels, would bring a different perspective to the analysis of long-term monitoring. Depicting a sound scene through the most probable sound pressure level, rather than portions of energy, brought more specific information about the activity carried out during the measurements. The statistical mode of the occurrences can capture typical behaviors of specific kinds of sound sources. The present work aims to propose an ML-based method to identify, separate and measure coexisting sound sources in real-world scenarios. It is based on long-term monitoring and is addressed to acousticians focused on the analysis of environmental noise in manifold contexts. The presented method is based on clustering analysis. Two algorithms, Gaussian Mixture Model and K-means clustering, represent the main core of a process to investigate different active spaces monitored through sound level meters. The procedure has been applied in two different contexts: university lecture halls and offices. The proposed method shows robust and reliable results in describing the acoustic scenario and it could represent an important analytical tool for acousticians

Investigating supra-intelligibility aspects of speech

158 p.Synthetic and recorded speech form a great part of oureveryday listening experience, and much of our exposure tothese forms of speech occurs in potentially noisy settings such as on public transport, in the classroom or workplace, while driving, and in our homes. Optimising speech output to ensure that salient information is both correctly and effortlessly received is a main concern for the designers of applications that make use of the speech modality. Most of the focus in adapting speech output to challenging listening conditions has been on intelligibility, and specifically on enhancing intelligibility by modifying speech prior to presentation. However, the quality of the generated speech is not always satisfying for the recipient, which might lead to fatigue, or reluctance in using this communication modality. Consequently, a sole focus on intelligibility enhancement provides an incomplete picture of a listener¿s experience since the effect of modified or synthetic speech on other characteristics risks being ignored. These concerns motivate the study of 'supra-intelligibility' factors such as the additional cognitive demand that modified speech may well impose upon listeners, as well as quality, naturalness, distortion and pleasantness. This thesis reports on an investigation into two supra-intelligibility factors: listening effort and listener preferences. Differences in listening effort across four speech types (plain natural, Lombard, algorithmically-enhanced, and synthetic speech) were measured using existing methods, including pupillometry, subjective judgements, and intelligibility scores. To explore the effects of speech features on listener preferences, a new tool, SpeechAdjuster, was developed. SpeechAdjuster allows the manipulation of virtually any aspect of speech and supports the joint elicitation of listener preferences and intelligibility measures. The tool reverses the roles of listener and experimenter by allowing listeners direct control of speech characteristics in real-time. Several experiments to explore the effects of speech properties on listening preferences and intelligibility using SpeechAdjuster were conducted. Participants were permitted to change a speech feature during an open-ended adjustment phase, followed by a test phase in which they identified speech presented with the feature value selected at the end of the adjustment phase. Experiments with native normal-hearing listeners measured the consequences of allowing listeners to change speech rate, fundamental frequency, and other features which led to spectral energy redistribution. Speech stimuli were presented in both quiet and masked conditions. Results revealed that listeners prefer feature modifications similar to those observed in naturally modified speech in noise (Lombard speech). Further, Lombard speech required the least listening effort compared to either plain natural, algorithmically-enhanced, or synthetic speech. For stationary noise, as noise level increased listeners chose slower speech rates and flatter tilts compared to the original speech. Only the choice of fundamental frequency was not consistent with that observed in Lombard speech. It is possible that features such as fundamental frequency that talkers naturally modify are by-products of the speech type (e.g. hyperarticulated speech) and might not be advantageous for the listener.Findings suggest that listener preferences provide information about the processing of speech over and above that measured by intelligibility. One of the listeners¿ concerns was to maximise intelligibility. In noise, listeners preferred the feature values for which more information survived masking, choosing speech rates that led to a contrast with the modulation rate of the masker, or modifications that led to a shift of spectral energy concentration to higher frequencies compared to those of the masker. For all features being modified by listeners, preferences were evident even when intelligibility was at or close to ceiling levels. Such preferences might result from a desire to reduce the cognitive effort of understanding speech, or from a desire to reproduce the sound of typical speech features experienced in real-world noisy conditions, or to optimise the quality of the modified signal. Investigation of supra-intelligibility aspects of speech promises to improve the quality of speech enhancement algorithms, bringing with it the potential of reducing the effort of understanding artificially-modified or generated forms of speech