Text-to-speech technology has made spectacular progress, offering natural voices in dozens of languages. However, not all languages are equal when it comes to speech synthesis. Some present unique linguistic challenges that make them particularly difficult to model. Let's find out why some languages give TTS engines more trouble than others.
Tonal languages
Tonal languages, such as Mandarin, Cantonese, Vietnamese, or Thai, are among the most difficult to synthesize. In these languages, the meaning of a word can change completely depending on the pitch (or tone) at which it is pronounced. For example, in Mandarin, the sound "ma" can mean "mother", "hemp", "horse", or "to scold" depending on the tone used. A TTS engine must not only pronounce the sound correctly, but also apply the correct tone according to the context, which is a major challenge.
Agglutinative languages
Agglutinative languages, such as Turkish, Finnish, Hungarian, or Japanese, have the particularity of forming very long words by adding a series of prefixes and suffixes to a root. A single word can thus correspond to a whole sentence in English. For example, in Turkish, the word "Çekoslovakyalılaştıramadıklarımızdan mısınız" means "Are you one of those that we could not Czechoslovakianize?". For a TTS engine, this means that it must be able to break down these complex words to understand their structure and pronunciation, which is much more difficult than working with shorter, more isolated words.
Languages with complex writing systems
Some languages have writing systems that do not correspond directly to pronunciation. In Arabic, for example, short vowels are generally not written, and the reader must deduce them from the context. A TTS engine must therefore be able to analyze the grammar and context to guess the missing vowels and pronounce the word correctly. Similarly, languages like Japanese use several writing systems (kanji, hiragana, katakana) that must be interpreted correctly.
Lack of data
Finally, one of the biggest obstacles to high-quality speech synthesis for many languages is simply the lack of data. Neural TTS models need huge amounts of audio recordings to be trained. For less common or less studied languages, it can be very difficult to collect enough data to create a high-performing model. This is why the quality of speech synthesis is often better for languages like English, Spanish, or Mandarin, for which data is abundant.
Conclusion
Speech synthesis is a constantly evolving field, and researchers are working tirelessly to overcome these linguistic challenges. Thanks to new model architectures, to few-shot learning techniques, and to efforts to collect data in a greater number of languages, high-quality speech synthesis is gradually becoming a reality for all the world's languages, and not just the most common ones.