How Alexa Mastered the Art of Speaking with an Enchanting Irish Accent
Introduction:
Speech synthesis technology has made significant advancements in the past few years, allowing for separate control of various elements of speech such as prosody, accent, language, and speaker identity (voice). For instance, this technology enabled the development of the English-language Alexa voice that can speak with a perfectly accented U.S. Spanish or British accent. However, when it came to creating an Irish-accented Alexa voice, there were challenges due to a lack of data. To address this, researchers at Amazon used a voice conversion model to produce training data in the target voice, resulting in improved accent quality. The use of a multiaccent model and additional information about phoneme durations further enhanced the natural-sounding synthetic speech. Comparisons with recordings of Irish English speakers showed that the synthesized speech closely approximated the average Irish accent. This new approach demonstrated a 50% improvement in accent similarity over prior methods.
Full Article: How Alexa Mastered the Art of Speaking with an Enchanting Irish Accent
Speech synthesis technology has made significant advancements in the past five years. The introduction of neural models has allowed for the separate control of various elements of speech, including prosody, accent, language, and speaker identity. This breakthrough has enabled the development of voices like the feminine-sounding English-language Alexa voice, which can speak in perfectly accented U.S. Spanish, and the masculine-sounding U.S. voice, which can speak with a British accent.
Two important factors contribute to the success of speech conversion and data augmentation: abundant annotated speech samples with the target accent and defined rules for mapping graphemes to phonemes. However, when it came to creating an Irish-accented English Alexa voice, these advantages were not present. The dataset was much smaller, and there were no grapheme-to-phoneme rules for Irish-accented English.
To overcome these challenges, Amazon researchers took a different approach. Rather than teaching an existing voice a new accent, they used recordings of accented speech and changed the speaker identity. This provided additional training data for the Irish-accent text-to-speech model, greatly improving the accent quality. Additionally, a voice conversion model was used to produce more Irish-accented training data in the target voice.
The multiaccent, multispeaker text-to-speech model utilized speaker embeddings, mel-spectrograms, and phoneme sequences as inputs during training. At inference time, the model received an accent ID signal to control the accent of the output speech. The use of a multiaccent model resulted in more natural-sounding synthetic speech compared to single-accent models.
The model’s inputs also included information about the durations of individual phonemes, allowing for better control of accent rhythm. A separate duration model predicted the phoneme durations at inference time. Despite the lack of grapheme-to-phoneme rules for Irish-accented English, the researchers experimented with both British English and American English rules and obtained credible results with the latter due to similarities in rhoticity between American English and Irish English.
To evaluate the method, reviewers compared the synthesized Irish English speech to recordings of different Irish English speakers. The accent similarity was rated at 61.4%, suggesting that the synthesized speech approximated the “average” Irish accent similarly to the source speaker. Comparisons with other Irish English speakers showed a similarity score of 53%, indicating the diversity of accents within Irish English.
Compared to the leading prior approach, the Amazon researchers’ method offered a 50% improvement in accent similarity. The results demonstrate the effectiveness of their approach in creating high-quality synthetic speech with accurate accents.
Acknowledgements were given to Andre Canelas for identifying the project opportunity, as well as Dennis Stansbury, Seán Mac Aodha, Laura Teefy, and Rikki Price for their support in making the experience authentic.
Summary: How Alexa Mastered the Art of Speaking with an Enchanting Irish Accent
In recent years, speech synthesis technology has advanced to allow for the separate control of speech elements such as prosody, accent, language, and speaker identity. This technology has enabled the creation of synthetic voices with different accents and languages. However, when it came to developing an Irish-accented English voice for Alexa, the existing methods did not yield satisfactory results. Instead, researchers used voice conversion models and additional training data to improve the accent quality of the Irish-accented voice. The use of multiaccent models and phoneme duration prediction models also contributed to more natural-sounding synthetic speech. Reviewers found that the synthesized Irish-accented speech closely approximated the “average” Irish accent. Notably, this approach offered a 50% improvement in accent similarity over the leading prior approach.
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