Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture

Schuller, Björn W.; Rigoll, Gerhard; Lang, M.

doi:10.1109/icassp.2004.1326051

Cited by 290 publications

(166 citation statements)

References 3 publications

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“…In the field of emotion recognition, the linguistic component was successfully combined with prosodic information in [5]. Their approach was to use a Belief Network for spotting of emotional key-phrases, based on their frequency of appearance in emotional utterances.…”

Section: Related Workmentioning

confidence: 99%

Aggression Detection in Speech Using Sensor and Semantic Information

Lefter

Rothkrantz

Burghouts

2012

Text, Speech and Dialogue

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Abstract. By analyzing a multimodal (audio-visual) database with aggressive incidents in trains, we have observed that there are no trivial fusion algorithms to successfully predict multimodal aggression based on unimodal sensor inputs. We proposed a fusion framework that contains a set of intermediate level variables (meta-features) between the low level sensor features and the multimodal aggression detection [1]. In this paper we predict the multimodal level of aggression and two of the meta-features: context and semantics. We do this based on the audio stream, from which we extract both nonverbal and verbal information. Given the spontaneus nature of speech in the database, we rely on a keyword spotting approach in the case of verbal information. We have found the existence of 6 semantic groups of keywords that have a positive influence on the prediction of aggression and of the two meta-features.

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Section: Related Workmentioning

confidence: 99%

Aggression Detection in Speech Using Sensor and Semantic Information

Lefter

Rothkrantz

Burghouts

2012

Text, Speech and Dialogue

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“…Call centers employ emotion classification to prioritize impatient customers Gupta and Rajput (2007) Lee and Narayanan (2005). Warning systems have been developed to detect aggressive driving Al Machot et al (2011) or to keep the driver alert Schuller et al (2004). In the healthcare field, emotion classification is used by clinicians for assessment or treatment of patients with psychological disorders or conditions that create emotional difficulties, such as autism or depression Tacconi et al (2008) Cowie et al (2000).…”

Section: Introductionmentioning

confidence: 99%

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

Yang

Yuan

Zhou

et al. 2016

Int J Speech Technol

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As an essential approach to understanding human interactions, emotion classification is a vital component of behavioral studies as well as being important in the design of context-aware systems. Recent studies have shown that speech contains rich information about emotion, and numerous speech-based emotion classification methods have been proposed. However, the classification performance is still short of what is desired for the algorithms to be used in real systems. We present an emotion classification system using several one-againstall support vector machines with a thresholding fusion mechanism to combine the individual outputs, which provides the functionality to effectively increase the emotion classification accuracy at the expense of rejecting some samples as unclassified. Results show that the proposed system outperforms three stateof-the-art methods and that the thresholding fusion mechanism can effectively improve the emotion classification, which is important for applications that require very high accuracy but do not require that all samples be classified.We evaluate the system performance for several challenging scenarios including speaker-independent tests, tests on noisy speech signals, and tests using non-professional acted recordings, in order to demonstrate the performance of the system and the effectiveness of the thresholding fusion mechanism in real scenarios.

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“…Provided that a person utters what he or she feels without hesitation in a controlled therapy setting, a very valuable piece of information is obtained that can be used for estimating the emotional state of a person through semantic analysis (e.g. keywords and n-grams) [12,13]. The emotional state estimator is part of an ongoing research project: Adaptive control of virtual reality scenarios in therapy of posttraumatic stress disorder (PTSD), at University of Zagreb [14,15].…”

Section: Introductionmentioning

confidence: 99%

Development of Acoustic Model for Croatian Language Using HTK

Dropuljić¹,

Petrinovic

2010

Automatika

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Original scientific paperPaper presents development of the acoustic model for Croatian language for automatic speech recognition (ASR). Continuous speech recognition is performed by means of the Hidden Markov Models (HMM) implemented in the HMM Toolkit (HTK). In order to adjust the HTK to the native language a novel algorithm for Croatian language transcription (CLT) has been developed. It is based on phonetic assimilation rules that are applied within uttered words. Phonetic questions for state tying of different triphone models have also been developed. The automated system for training and evaluation of acoustic models has been developed and integrated with the new graphical user interface (GUI). Targeted applications of this ASR system are stress inoculation training (SIT) and virtual reality exposure therapy (VRET). Adaptability of the model to a closed set of speakers is important for such applications and this paper investigates the applicability of the HTK tool for typical scenarios. Robustness of the tool to a new language was tested in matched conditions by a parallel training of an English model that was used as a baseline. Ten native Croatian speakers participated in experiments. Encouraging results were achieved and reported with the developed model for Croatian language.Key words: Acoustic model, Automatic speech recognition, Croatian language, Hidden Markov models, Phonetic assimilation, Phonetic transcription algorithm, Recognition accuracy Razvoj akustičkog modela hrvatskog jezika pomoću alata HTK. Rad opisuje razvoj akustičkog modela hrvatskog jezika za potrebe sustava za automatsko prepoznavanje govora. Prepoznavanje prirodnog spojenog izgovora ostvaruje se korištenjem skrivenih Markovljevih modela (HMM) u okviru alata HTK. U svrhu prilagodbe ovog alata na hrvatski jezik razvijen je novi algoritam za automatsku fonetsku transkripciju hrvatskih riječi. Zasniva se na načelu fonetske asimilacije unutar izgovorenih riječi. Razvijen je i skup fonetskih pitanja koji se koristi za klasifikaciju prilikom udruživanja trifonskih modela sličnih glasova. Razvijena je automatizirana aplikacija za gradnju i evaluaciju akustičkih modela, integrirana s novo razvijenim grafičkim sučeljem. Primjene ovog sustava za prepoznavanje su trening s doziranim izlaganjem stresu (SIT) i terapija izlaganjem primjenom virtualne stvarnosti (VRET). Prilagodljivost akustičkog modela na zatvoren skup govornika vrlo je važna za takve primjene, pa se u radu istražuje primjenjivost alata HTK u tipičnim scenarijima. Robusnost alata na promjenu jezika istražuje se uparenim treniranjem i evaluacijom ekvivalentnog modela engleskog jezika u jednakim uvjetima. U eksperimentima je sudjelovalo deset izvornih hrvatskih govornika. Ostvareni rezultati za hrvatski jezik prikazani u radu pokazuju zadovoljavajuća svojstva razvijenog akustičkog modela hrvatskog jezika.Ključne riječi: akustički model, automatsko prepoznavanje govora, hrvatski jezik, skriveni Markovljevi modeli, algoritam za fonetsku transkripciju, fonetska asimilacija, točnost pr...

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Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture

Cited by 290 publications

References 3 publications

Aggression Detection in Speech Using Sensor and Semantic Information

Aggression Detection in Speech Using Sensor and Semantic Information

Enhanced multiclass SVM with thresholding fusion for speech-based emotion classification

Development of Acoustic Model for Croatian Language Using HTK

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