ERA Collection:

EDINBURGH COGNITIVE AND BEHAVIOURAL ALS SCREEN – ECAS English version 2013

2013-05-21T09:04:20Z

Title: EDINBURGH COGNITIVE AND BEHAVIOURAL ALS SCREEN – ECAS English version 2013 Authors: Abrahams, Sharon; Bak, Thomas Abstract: to be added

Whole genome association scan for genetic polymorphisms influencing information processing speed.

2013-04-10T10:52:53Z

Title: Whole genome association scan for genetic polymorphisms influencing information processing speed. Authors: Luciano, Michelle; Hansell, Narelle; Lahti, Jari; Davies, Gail; Medland, Sarah E; Räikkönen, Katri; Tenesa, Albert; Widen, Elisabeth; McGhee, Kevin A; Palotie, Aarno; Liewald, David; Porteous, David; Starr, John M; Montgomery, Grant; Martin, Nicholas G; Eriksson, Johan G; Wright, Margaret J; Deary, Ian J Abstract: Processing speed is an important cognitive function that is compromised in psychiatric illness (e.g., schizophrenia, depression) and old age; it shares genetic background with complex cognition (e.g., working memory, reasoning). To find genes influencing speed we performed a genome-wide association scan in up to three cohorts: Brisbane (mean age 16 years; N=1659); LBC1936 (mean age 70 years, N=992); LBC1921 (mean age 82 years, N=307), and; HBCS (mean age 64 years, N=1080). Meta-analysis of the common measures highlighted various suggestively significant (p<1.21x10-5) SNPs and plausible candidate genes (e.g., TRIB3). A biological pathways analysis of the speed factor identified two common pathways from the KEGG database (cell junction, focal adhesion) in two cohorts, while a pathway analysis linked to the GO database revealed common pathways across pairs of speed measures (e.g., receptor binding, cellular metabolic process). These highlighted genes and pathways will be able to inform future research, including results for psychiatric disease. Description: This work was supported by the Biotechnology and Biological Sciences Research Council [grant number BB/F019394/1]; grant number BB/F019394/1

Thousands of Voices for HMM-Based Speech Synthesis-Analysis and Application of TTS Systems Built on Various ASR Corpora

2010-09-28T13:52:16Z

Title: Thousands of Voices for HMM-Based Speech Synthesis-Analysis and Application of TTS Systems Built on Various ASR Corpora Authors: Yamagishi, Junichi; Usabaev, Bela; King, Simon; Watts, Oliver; Dines, John; Tian, Jilei; Guan, Yong; Hu, Rile; Oura, Keiichiro; Wu, Yi-Jian; Tokuda, Keiichi; Karhila, Reima; Kurimo, Mikko Abstract: In conventional speech synthesis, large amounts of phonetically balanced speech data recorded in highly controlled recording studio environments are typically required to build a voice. Although using such data is a straightforward solution for high quality synthesis, the number of voices available will always be limited, because recording costs are high. On the other hand, our recent experiments with HMM-based speech synthesis systems have demonstrated that speaker-adaptive HMM-based speech synthesis (which uses an "average voice model" plus model adaptation) is robust to non-ideal speech data that are recorded under various conditions and with varying microphones, that are not perfectly clean, and/or that lack phonetic balance. This enables us to consider building high-quality voices on "non-TTS" corpora such as ASR corpora. Since ASR corpora generally include a large number of speakers, this leads to the possibility of producing an enormous number of voices automatically. In this paper, we demonstrate the thousands of voices for HMM-based speech synthesis that we have made from several popular ASR corpora such as the Wall Street Journal (WSJ0, WSJ1, and WSJCAM0), Resource Management, Globalphone, and SPEECON databases. We also present the results of associated analysis based on perceptual evaluation, and discuss remaining issues.

Synthesis of Child Speech With HMM Adaptation and Voice Conversion

2010-09-27T10:47:36Z

Title: Synthesis of Child Speech With HMM Adaptation and Voice Conversion Authors: Watts, Oliver; Yamagishi, Junichi; King, Simon; Berkling, Kay Abstract: The synthesis of child speech presents challenges both in the collection of data and in the building of a synthesizer from that data. We chose to build a statistical parametric synthesizer using the hidden Markov model (HMM)-based system HTS, as this technique has previously been shown to perform well for limited amounts of data, and for data collected under imperfect conditions. Six different configurations of the synthesizer were compared, using both speaker-dependent and speaker-adaptive modeling techniques, and using varying amounts of data. For comparison with HMM adaptation, techniques from voice conversion were used to transform existing synthesizers to the characteristics of the target speaker. Speaker-adaptive voices generally outperformed child speaker-dependent voices in the evaluation. HMM adaptation outperformed voice conversion style techniques when using the full target speaker corpus; with fewer adaptation data, however, no significant listener preference for either HMM adaptation or voice conversion methods was found.