Most Text to Speech (TTS) systems today assume that the input is in a single language written in its native script, which is the language that the TTS database is recorded in. However, due to the rise in conversational data available from social media, phenomena such as code-mixing, in which multiple languages
\nare used together in the same conversation or sentence are now seen in text. TTS systems capable of synthesizing such text need to be able to handle multiple languages at the same time, and may also need to deal with noisy input. Previously, we proposed a framework to synthesize code-mixed text by using a TTS database in a single language, identifying the language that each word was from, normalizing spellings of a language written in a non-standardized script and mapping the phonetic space of mixed language to the language that the TTS database was recorded in. We extend this cross-lingual approach to more language pairs, and improve upon our language identification technique. We conduct listening tests to determine which of the two languages being mixed should be used as the target language.
\nWe perform experiments for code-mixed Hindi-English and German-English and conduct listening tests with bilingual speakers of these languages. From our subjective experiments we find that listeners have a strong preference for cross-lingual systems with Hindi as the target language for code-mixed Hindi and English text. We also find that listeners prefer cross-lingual systems in English that can synthesize German text for codemixed German and English text.<\/p>\n","protected":false},"excerpt":{"rendered":"
Most Text to Speech (TTS) systems today assume that the input is in a single language written in its native script, which is the language that the TTS database is recorded in. However, due to the rise in conversational data available from social media, phenomena such as code-mixing, in which multiple languages are used together […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-424584","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Speech Synthesis Workshop 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Krishna Rallabandi","user_id":0,"rest_url":false},{"type":"text","value":"Shruti Rijhwani","user_id":0,"rest_url":false},{"type":"text","value":"Alan W Black","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[199562],"msr_event":[],"msr_group":[144940],"msr_project":[248216],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":248216,"post_title":"Project Melange","post_name":"melange","post_type":"msr-project","post_date":"2016-07-04 04:02:18","post_modified":"2022-04-29 10:24:28","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/melange\/","post_excerpt":"The goal of Project M\u00e9lange is to understand the uses of and build tools around code-mixing, analyze and understand code-switching behavior, and equip speech and language processing systems with the capabilities of processing, understanding and generating code-switched 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