1 research outputs found
Pronunciation modelling in end-to-end text-to-speech synthesis
Sequence-to-sequence (S2S) models in text-to-speech synthesis (TTS) can achieve
high-quality naturalness scores without extensive processing of text-input. Since S2S
models have been proposed in multiple aspects of the TTS pipeline, the field has focused
on embedding the pipeline toward End-to-End (E2E-) TTS where a waveform
is predicted directly from a sequence of text or phone characters. Early work on E2ETTS
in English, such as Char2Wav [1] and Tacotron [2], suggested that phonetisation
(lexicon-lookup and/or G2P modelling) could be implicitly learnt in a text-encoder
during training. The benefits of a learned text encoding include improved modelling
of phonetic context, which make contextual linguistic features traditionally used in
TTS pipelines redundant [3]. Subsequent work on E2E-TTS has since shown similar
naturalness scores with text- or phone-input (e.g. as in [4]). Successful modelling
of phonetic context has led some to question the benefit of using phone- instead of
text-input altogether (see [5]).
The use of text-input brings into question the value of the pronunciation lexicon
in E2E-TTS. Without phone-input, a S2S encoder learns an implicit grapheme-tophoneme
(G2P) model from text-audio pairs during training. With common datasets
for E2E-TTS in English, I simulated implicit G2P models, finding increased error rates
compared to a traditional, lexicon-based G2P model. Ultimately, successful G2P generalisation
is difficult for some words (e.g. foreign words and proper names) since
the knowledge to disambiguate their pronunciations may not be provided by the local
grapheme context and may require knowledge beyond that contained in sentence-level
text-audio sequences. When test stimuli were selected according to G2P difficulty,
increased mispronunciations in E2E-TTS with text-input were observed. Following
the proposed benefits of subword decomposition in S2S modelling in other language
tasks (e.g. neural machine translation), the effects of morphological decomposition
were investigated on pronunciation modelling. Learning of the French post-lexical
phenomenon liaison was also evaluated.
With the goal of an inexpensive, large-scale evaluation of pronunciation modelling,
the reliability of automatic speech recognition (ASR) to measure TTS intelligibility
was investigated. A re-evaluation of 6 years of results from the Blizzard Challenge
was conducted. ASR reliably found similar significant differences between systems
as paid listeners in controlled conditions in English. An analysis of transcriptions for
words exhibiting difficult-to-predict G2P relations was also conducted. The E2E-ASR
Transformer model used was found to be unreliable in its transcription of difficult G2P
relations due to homophonic transcription and incorrect transcription of words with
difficult G2P relations. A further evaluation of representation mixing in Tacotron finds
pronunciation correction is possible when mixing text- and phone-inputs. The thesis
concludes that there is still a place for the pronunciation lexicon in E2E-TTS as a
pronunciation guide since it can provide assurances that G2P generalisation cannot