SEMOUR: A Scripted Emotional Speech Repository for Urdu

  title={SEMOUR: A Scripted Emotional Speech Repository for Urdu},
  author={Nimra Zaheer and O. Ahmad and Ammar Ahmed and Muhammad Shehryar Khan and Mudassir Shabbir},
  journal={Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
Designing reliable Speech Emotion Recognition systems is a complex task that inevitably requires sufficient data for training purposes. Such extensive datasets are currently available in only a few languages, including English, German, and Italian. In this paper, we present SEMOUR, the first scripted database of emotion-tagged speech in the Urdu language, to design an Urdu Speech Recognition System. Our gender-balanced dataset contains 15,040 unique instances recorded by eight professional… Expand

Figures and Tables from this paper


ShEMO: a large-scale validated database for Persian speech emotion detection
A large-scale, validated database for Persian called Sharif Emotional Speech Database (ShEMO), which covers speech samples of 87 native-Persian speakers for five basic emotions including anger, fear, happiness, sadness and surprise, as well as neutral state is introduced. Expand
Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
This study investigates the problem of cross-lingual emotion recognition for Urdu language and contributes URDU—the first ever spontaneous Urdu-language speech emotion database and suggests various interesting aspects for designing more adaptive emotion recognition system for such limited languages. Expand
EMOVO Corpus: an Italian Emotional Speech Database
It is observed that emotions less easy to recognize are joy and disgust, whereas the most easy to detect are anger, sadness and the neutral state. Expand
The Vera am Mittag German audio-visual emotional speech database
This contribution presents a recently collected database of spontaneous emotional speech in German which is being made available to the research community and provides emotion labels for a great part of the data. Expand
A database of German emotional speech
A database of emotional speech that was evaluated in a perception test regarding the recognisability of emotions and their naturalness and can be accessed by the public via the internet. Expand
CHEAVD: a Chinese natural emotional audio–visual database
This database is the first large-scale Chinese natural emotion corpus dealing with multimodal and natural emotion, and free to research use, and Automatic emotion recognition with Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) is performed on this corpus. Expand
Recognizing emotion from Turkish speech using acoustic features
A new Turkish emotional speech database, which includes 5,100 utterances extracted from 55 Turkish movies, was constructed and promising results for activation and dominance dimensions were obtained. Expand
VESUS: A Crowd-Annotated Database to Study Emotion Production and Perception in Spoken English
This work provides benchmark performance on three distinct emotion recognition tasks using VESUS: longitudinal speaker analysis, extrapolating across syntactical complexity, and generalization to a new speaker. Expand
PronouncUR: An Urdu Pronunciation Lexicon Generator
This paper presents a grapheme-to-phoneme conversion tool for Urdu that generates a pronunciation lexicon in a form suitable for use with speech recognition systems from a list of Urdu words. Expand
IEMOCAP: interactive emotional dyadic motion capture database
A new corpus named the “interactive emotional dyadic motion capture database” (IEMOCAP), collected by the Speech Analysis and Interpretation Laboratory at the University of Southern California (USC), which provides detailed information about their facial expressions and hand movements during scripted and spontaneous spoken communication scenarios. Expand