Jaime Lorenzo-Trueba

Segmenting human activities based on HMMs using smartphone inertial sensors

This paper describes the development of a Human Activity Recognition and Segmentation (HARS) system based on Hidden Markov Models (HMMs). This system uses inertial signals from a smartphone to recognize and segment six different physical activities: walking, walking-upstairs, walking-downstairs, sitting, standing and lying down. All the experiments have been done using a publicly available dataset called UCI Human Activity Recognition Using Smartphones. The developed system improves the results obtained on this dataset in previous works. The main contribution of this paper is the incorporation of an Activity Sequence Model. The best results show an Activity Segmentation Error Rate of 2.1%. Human Activity Recognition and Segmentation using Hidden Markov Models.Hidden Markov Models configuration analysis.Activity sequence modeling.

Towards Cross-Lingual Emotion Transplantation

In this paper we introduce the idea of cross-lingual emotion transplantation. The aim is to lean the nuances of emotional speech in a source language for which we have enough data to adapt an acceptable quality emotional model by means of CSMAPLR adaptation, and then convert the adaptation function so it can be applied to a target language in a different target speaker while maintaining the speaker identity but adding emotional information. The conversion between languages is done at state level by measuring the KLD distance between the Gaussian distributions of all the states and linking the closest ones. Finally, as the cross-lingual transplantation of spectral emotions mainly anger was found out to introduce significant amounts of spectral noise, we show the results of applying three different techniques related to adaptation parameters that can be used to reduce the noise. The results are measured in an objective fashion by means of a bi-dimensional PCA projection of the KLD distances between the considered models neutral models of both languages, reference emotion for both languages and transplanted emotional model for the target language.

Improving Spanish speech synthesis intelligibility under noisy environments

In this paper, we evaluate a newly recorded Spanish Lombard Speech database. This database has been recorded with expressive speech synthesis in mind, and more particularly adaptability to the environment. Real stationary noise recorded inside of a car was used to produce the Lombard speech response in the speaker by means of a headphone. Four different noise levels were used, in steps of 5 dB and also clean speech to set the clean speech baseline. Finally, a pair of intelligibility evaluations were carried out, one with natural speech that proves the validity of the recorded database by showing a 37% absolute increase in intelligibility in a -10 dB SNR condition when compared to non-Lombard speech. The second evaluation was carried out with synthetic speech, which showed a 10% absolute increase in intelligibility for both the -10 and -15 dB SNR condition.