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Despite the great advances, most of the recently developed automatic speech recognition systems focus on working in a server-client manner, and thus often require a high computational cost, such as the storage size and memory accesses. This, however, does not satisfy the increasing demand for a succinct model that can run smoothly in embedded devices like smartphones.

Conventional feature-based classification methods do not apply well to automatic recognition of speech emotions, mostly because the precise set of spectral and prosodic features that is required to identify the emotional state of a speaker has not been determined yet. This paper presents a method that operates directly on the speech signal, thus avoiding the problematic step of feature extraction.