http://www.individual.utoronto.ca/falkt/falk/pdf/Wu_SpeCom2010.pdf

Automatic speech emotion recognition using modulation spectral features

As the recognition of emotion from speech has matured to a degree where it becomes applicable in real-life settings, it is time for a realistic view on obtainable performances. Most studies tend to overestimation in this respect: Acted data is often used rather than spontaneous data, results are reported on preselected prototypical data, and true speaker disjunctive partitioning is still less common than simple cross-validation. Even speaker disjunctive evaluation can give only a little insight into the generalization ability of today's emotion recognition engines since training and test data used for system development usually tend to be similar as far as recording conditions, noise overlay, language, and types of emotions are concerned. A considerably more realistic impression can be gathered by interset evaluation: We therefore show results employing six standard databases in a cross-corpora evaluation experiment which could also be helpful for learning about chances to add resources for training and overcoming the typical sparseness in the field. To better cope with the observed high variances, different types of normalization are investigated. 1.8 k individual evaluations in total indicate the crucial performance inferiority of inter to intracorpus testing.

Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies

In this paper, we describe new results and improvements to a lan- guage identification (LID) system based on PPRLM previously introduced in (1) and (2). In this case, we use as parallel phone recognizers the ones provided by the Brno University of Technology for Czech, Hungarian, and Russian lan- guages, and instead of using traditional n-gram language models we use a lan- guage model that is created using a ranking with the most frequent and discrim- inative n-grams. In this language model approach, the distance between the ranking for the input sentence and the ranking for each language is computed, based on the difference in relative positions for each n-gram. This approach is able to model reliably longer span information than in traditional language models obtaining more reliable estimations. We also describe the modifications that we have being introducing along the time to the original ranking technique, e.g., different discriminative formulas to establish the ranking, variations of the template size, the suppression of repeated consecutive phones, and a new clus- tering technique for the ranking scores. Results show that this technique pro- vides a 12.9% relative improvement over PPRLM. Finally, we also describe re- sults where the traditional PPRLM and our ranking technique are combined.

/pdf/language-identification-based-on-a-discriminative-text-12gmhrsjwn.pdf

Language Identification based on a Discriminative Text Categorization Technique

We investigate different approaches for dialect identification in Arabic broadcast speech, using phonetic, lexical features obtained from a speech recognition system, and acoustic features using the i-vector framework. We studied both generative and discriminate classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic/English language identification task, with an accuracy of 100%. We used these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We further report results using the proposed method to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 52%. We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. We also release the train and test data as standard corpus for dialect identification.

Automatic Dialect Detection in Arabic Broadcast Speech

http://www.geintra-uah.org/system/files/2010-FinalPaperBarraSpecom.pdf

Analysis of statistical parametric and unit selection speech synthesis systems applied to emotional speech

http://www-gth.die.upm.es/research/documentation/AI-106Spe-08.pdf

Speech to sign language translation system for Spanish

It is well known that the emotional state of a speaker usually alters the way she/he speaks. Although all the components of the voice can be affected by emotion in some statistically-significant way, not all these deviations from a neutral voice are identified by human listeners as conveying emotional information. In this paper we have carried out several perceptual and objective experiments that show the relevance of prosody and segmental spectrum in the characterization and identification of four emotions in Spanish. A Bayes classifier has been used in the objective emotion identification task. Emotion models were generated as the contribution of every emotion to the build-up of a universal background emotion codebook. According to our experiments, surprise is primarily identified by humans through its prosodic rubric (in spite of some automatically-identifiable segmental characteristics); while for anger the situation is just the opposite. Sadness and happiness need a combination of prosodic and segmental rubrics to be reliably identified

/pdf/prosodic-and-segmental-rubrics-in-emotion-identification-1tw0nnwuu5.pdf

Prosodic and Segmental Rubrics in Emotion Identification

In this paper, a Bayesian networks, BNs, approach to dialogue modelling is evaluated in terms of a battery of both subjective and objective metrics. A significant effort in improving the contextual information handling capabilities of the system has been done. Consequently, besides typical dialogue measurement rates for usability like task or dialogue completion rates, dialogue time, etc. we have included a new figure measuring the contextuality of the dialogue as the number of turns where contextual information is helpful for dialogue resolution. The evaluation is developed through a set of predefined scenarios according to different initiative styles and focusing on the impact of the user's level of experience.

/pdf/evaluation-of-a-spoken-dialogue-system-for-controlling-a-4xh2v8il0c.pdf

Evaluation of a spoken dialogue system for controlling a Hifi audio system

This paper presents a set of experiments used to develop a statistical system from translating speech to sign language for deaf people. This system is composed of an Automatic Speech Recognition (ASR) system, followed by a statistical translation module and an animated agent that represents the different signs. Two different approaches have been used to perform the translations: a phrase-based system and a finite state transducer. For the evaluation, the followings figures have been considered: WER (Word Error Rate), BLEU and NIST. The paper presents translation results of reference sentences and sentences from the automatic speech recognizer. Also three different configurations have been evaluated for the speech recognizer. The best results were obtained with the finite state transducer, with a word error rate of 28.21% for the reference text, and 29.27% using the ASR output.

/pdf/telecomi-d04-speech-into-sign-language-statistical-4txt7s0soa.pdf

TelecomI+D04: Speech into Sign Language Statistical Translation System for Deaf People

In this paper, we present several innovative techniques that can be applied in a PPRLM system for language identification (LID). We will show how we obtained a 53.5% relative error reduction from our base system using several techniques. First, the application of a variable threshold in score computation, dependent on the average scores in the language model, provided a 35% error reduction. A random selection of sentences for the different sets and the use of silence models also improved the system. Then, to improve the classifier, we compared the bias removal technique (up to 19% error reduction) and a Gaussian classifier (up to 37% error reduction). Finally, we included the acoustic score in the Gaussian classifier (2% error reduction) and increased the number of Gaussians to have a multiple-Gaussian classifier (14% error reduction). We will show how all these improvements are remarkable as they have been mostly additive.

/pdf/integration-of-acoustic-information-and-pprlm-scores-in-a-jao4yv1ul9.pdf

R. Barra

Papers

Speech to sign language translation system for Spanish

Prosodic and Segmental Rubrics in Emotion Identification

Evaluation of a spoken dialogue system for controlling a Hifi audio system

TelecomI+D04: Speech into Sign Language Statistical Translation System for Deaf People

Integration of acoustic information and PPRLM scores in a multiple-Gaussian classifier for Language Identification