Automatic prosodic variations modelling for language and dialect discrimination

International audienceThis paper addresses the problem of modelling prosody for language identification. The aim is to create a system that can be used prior to any linguistic work to show if prosodic differences among languages or dialects can be automatically determined. In previous papers, we defined a prosodic unit, the pseudo-syllable. Rhythmic modelling has proven the relevance of the pseudo-syllable unit for automatic language identification. In this paper, we propose to model the prosodic variations, that is to say model sequences of prosodic units. This is achieved by the separation of phrase and accentual components of intonation. We propose an independent coding of those components on differentiated scales of duration. Short-term and long-term language-dependent sequences of labels are modelled by n-gram models. The performance of the system is demonstrated by experiments on read speech and evaluated by experiments on spontaneous speech. Finally, an experiment is described on the discrimination of Arabic dialects, for which there is a lack of linguistic studies, notably on prosodic comparisons. We show that our system is able to clearly identify the dialectal areas, leading to the hypothesis that those dialects have prosodic differences

Reviewing Human Language Identification

Abstract. This article overviews human language identification (LID) experiments, especially focusing on the modification methods of stimulus, mentioning the experimental designs and languages used. A variety of signals to represent prosody have been used as stimuli in perceptual experiments: lowpass-filtered speech, laryngograph output, triangular pulse trains or sinusoidal signals, LPC-resynthesized or residual signals, white-noise driven signals, resynthesized signals preserving or degrading broad phonotactics, syllabic rhythm, or intonation, and parameterized source component of speech signal. Although all of these experiments showed that "prosody" plays a role in LID, the stimuli differ from each other in the amount of information they carry. The article discusses the acoustic natures of these signals and some theoretical backgrounds, featuring the correspondence of the source, in terms of the sourcefilter theory, to prosody, from a linguistic perspective. It also reviews LID experiments using unmodified speech, research into infants, dialectology and sociophonetic research, and research into foreign accent

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Emotion recognition based on the energy distribution of plosive syllables

We usually encounter two problems during speech emotion recognition (SER): expression and perception problems, which vary considerably between speakers, languages, and sentence pronunciation. In fact, finding an optimal system that characterizes the emotions overcoming all these differences is a promising prospect. In this perspective, we considered two emotional databases: Moroccan Arabic dialect emotional database (MADED), and Ryerson audio-visual database on emotional speech and song (RAVDESS) which present notable differences in terms of type (natural/acted), and language (Arabic/English). We proposed a detection process based on 27 acoustic features extracted from consonant-vowel (CV) syllabic units: \ba, \du, \ki, \ta common to both databases. We tested two classification strategies: multiclass (all emotions combined: joy, sadness, neutral, anger) and binary (neutral vs. others, positive emotions (joy) vs. negative emotions (sadness, anger), sadness vs. anger). These strategies were tested three times: i) on MADED, ii) on RAVDESS, iii) on MADED and RAVDESS. The proposed method gave better recognition accuracy in the case of binary classification. The rates reach an average of 78% for the multi-class classification, 100% for neutral vs. other cases, 100% for the negative emotions (i.e. anger vs. sadness), and 96% for the positive vs. negative emotions