Using neutral speech models for emotional speech analysis

Carlos Busso · Sungbok Lee · Shrikanth Shri Narayanan

83 citations0/0 files storedInterspeech (USB)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Abstract Since emotional speech can be regarded as a variation onneutral (non-emotional) speech, it is expected that a robust neu-tral speech model can be useful in contrasting different emo-tions expressed in speech. This study explores this idea by cre-ating acoustic models trained with spectral features, using theemotionally-neutral TIMIT corpus. The performance is testedwith two emotional speech databases: one recorded with a mi-crophone (acted), and another recorded from a telephone ap-plication (spontaneous). It is found that accuracy up to 78%and 65% can be achieved in the binary and category emotiondiscriminations, respectively. Raw Mel Filter Bank (MFB) out-put was found to perform better than conventional MFCC, withboth broad-band and telephone-band speech. These results sug-gest that well-trained neutral acoustic models can be effectivelyused as a front-end for emotion recognition, and once trainedwith MFB, it may reasonably work well regardless of the chan-nel characteristics.Index Terms: Emotion recognition, Neutral speech, HMMs,Mel ﬁlter bank (MFB), TIMIT

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 83 citations exist upstream.

References

2001Emotion recognition in human-computer interaction 2005Toward detecting emotions in spoken dialogs 2003Toward an affect-sensitive multimodal human-computer interaction 2003Describing the emotional states that are expressed in speech 2007Primitives-based evaluation and estimation of emotions in speech 2004Emotion recognition based on phoneme classes 2004An acoustic study of emotions expressed in speech 2005An articulatory study of emotional speech production

Cited by

2021Emotion Recognition by Fusing Time Synchronous and Time Asynchronous Representations 2010Acoustic feature analysis in speech emotion primitives estimation 2012Using i-vector space model for emotion recognition 2015Speech emotion recognition in emotional feedback for Human-Robot Interaction 2020Fusion of deep learning features with mixture of brain emotional learning for audio-visual emotion recognition 2011Iterative feature normalization for emotional speech detection

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.