We present in this paper PADIC, a Parallel Arabic DIalect Corpus we built from scratch, then we conducted experiments on crossdialect Arabic machine translation. PADIC is composed of dialects from both the Maghreb and the Middle-East. Each dialect has been aligned with Modern Standard Arabic (MSA). Three dialects from Maghreb are concerned by this study: two from Algeria, one from Tunisia, and ...

The dialects of Madagascar belong to the Greater Barito East group of the Austronesian family and it is widely accepted that the Island was colonized by Indonesian sailors after a maritime trek that probably took place around 650 CE. The language most closely related to Malagasy dialects is Maanyan, but Malay is also strongly related especially for navigation terms. Since the Maanyan Dayaks liv...

The training data for statistical machine translation are gathered from various sources representing a mixture of domains. In this work, we argue that when translating dialects representing varieties of the same language, a manually assigned data source is not a reliable indicator of the dialect. We resort to automatic dialect classification to refine the training corpora according to the diffe...

Dialect variations of a language have a severe impact on the performance of speech systems. Therefore, knowing how close or separate dialects are in a given language space provides useful information to predict, or improve, system performance when there is a mismatch between train and test data. Distance measures have been used in several applications of speech processing, including speech reco...

This paper aims to design and implement speech recognition system with Discrete Wavelets transformation using Daubechies wavelets for analysis of continuous speech for similar sample sets. This work is based on analysis of Signals of Regional Dialects and finding similar pattern in the Regional Dialects of Demographic Region, which provides a highly reliable way for recognizing speech.The impor...

Dialect differences within a given language represent major challenges for sustained speech system performance. For speech recognition, little if any knowledge exists on differences between dialects (e.g. vocabulary, grammar, prosody, etc.). Effective dialect classification can contribute to improved ASR, speaker ID, and spoken document retrieval. This study, presents an approach to establish a...

The current study reports preliminary information gathered about First Nations English dialects in Canada and considers implications for speech-language pathology practice. Information was gathered from literature searches and forums of First Nations and non-First Nations speech-language pathologists, developmentalists, and linguists. The exploratory findings suggest that First Nations English ...

This project compares the accuracy of two automatic speech recognition (ASR) systems–Bing Speech and YouTube’s automatic captions–across gender, race and four dialects of American English. The dialects included were chosen for their acoustic dissimilarity. Bing Speech had differences in word error rate (WER) between dialects and ethnicities, but they were not statistically reliable. YouTube’s a...

Problem statement: In general, there are a number of rural dialects in Thai. However, four dialects are mainly spoken by Thai people residing in four core region including central, north, northeast and south regions. Recognizing and synthesizing Thai speech with different dialects are consequently difficult. Approach: Prosody is an important factor that must be taken into account, since the pro...