Voice conversion: transforming a speaker's voice while preserving linguistic content.
Voice conversion is a technology that aims to modify a speaker's voice to make it sound like another speaker's voice while keeping the linguistic content unchanged. This technology has gained popularity in various speech synthesis applications and has been approached using different techniques, such as neural networks and adversarial learning.
Recent research in voice conversion has focused on addressing challenges like working with non-parallel data, noisy training data, and zero-shot voice style transfer. Non-parallel data refers to the absence of corresponding pairs of source and target speaker utterances, making it difficult to train models. Noisy training data can degrade the voice conversion success, and zero-shot voice style transfer involves generating voices for previously unseen speakers.
One notable approach is the use of Cycle-Consistent Adversarial Networks (CycleGAN), which do not require parallel training data and have shown promising results in one-to-one voice conversion. Another approach is the Invertible Voice Conversion framework (INVVC), which allows for traceability of the source identity and can be applied to one-to-one and many-to-one voice conversion using parallel training data.
Practical applications of voice conversion include:
1. Personalizing text-to-speech systems: Voice conversion can be used to generate speech in a user's preferred voice, making the interaction more engaging and enjoyable.
2. Entertainment industry: Voice conversion can be applied in movies, animations, and video games to create unique character voices or dubbing in different languages.
3. Accessibility: Voice conversion can help individuals with speech impairments by converting their speech into a more intelligible voice, improving communication.
A company case study is DurIAN-SC, a singing voice conversion system that generates high-quality target speaker's singing using only their normal speech data. This system integrates the training and conversion process of speech and singing into one framework, making it more robust, especially when the singing database is small.
In conclusion, voice conversion technology has made significant progress in recent years, with researchers exploring various techniques to overcome challenges and improve performance. As the technology continues to advance, it is expected to find broader applications and contribute to more natural and engaging human-computer interactions.
Voice Conversion Further Reading1.MASS: Multi-task Anthropomorphic Speech Synthesis Framework http://arxiv.org/abs/2105.04124v1 Jinyin Chen, Linhui Ye, Zhaoyan Ming2.Vowels and Prosody Contribution in Neural Network Based Voice Conversion Algorithm with Noisy Training Data http://arxiv.org/abs/2003.04640v1 Olaide Agbolade3.Invertible Voice Conversion http://arxiv.org/abs/2201.10687v1 Zexin Cai, Ming Li4.Singing voice conversion with non-parallel data http://arxiv.org/abs/1903.04124v1 Xin Chen, Wei Chu, Jinxi Guo, Ning Xu5.Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning http://arxiv.org/abs/2103.09420v1 Siyang Yuan, Pengyu Cheng, Ruiyi Zhang, Weituo Hao, Zhe Gan, Lawrence Carin6.Identifying Source Speakers for Voice Conversion based Spoofing Attacks on Speaker Verification Systems http://arxiv.org/abs/2206.09103v2 Danwei Cai, Zexin Cai, Ming Li7.DurIAN-SC: Duration Informed Attention Network based Singing Voice Conversion System http://arxiv.org/abs/2008.03009v1 Liqiang Zhang, Chengzhu Yu, Heng Lu, Chao Weng, Chunlei Zhang, Yusong Wu, Xiang Xie, Zijin Li, Dong Yu8.NVC-Net: End-to-End Adversarial Voice Conversion http://arxiv.org/abs/2106.00992v1 Bac Nguyen, Fabien Cardinaux9.Many-to-Many Voice Conversion using Conditional Cycle-Consistent Adversarial Networks http://arxiv.org/abs/2002.06328v1 Shindong Lee, BongGu Ko, Keonnyeong Lee, In-Chul Yoo, Dongsuk Yook10.Beyond Voice Identity Conversion: Manipulating Voice Attributes by Adversarial Learning of Structured Disentangled Representations http://arxiv.org/abs/2107.12346v2 Laurent Benaroya, Nicolas Obin, Axel Roebel
Voice Conversion Frequently Asked Questions
What is the difference between voice conversion and voice cloning?
Voice conversion is the process of transforming a speaker's voice to sound like another speaker's voice while preserving the linguistic content. It aims to maintain the original message while changing the voice characteristics. Voice cloning, on the other hand, is the process of creating a synthetic voice that closely resembles a target speaker's voice. It involves training a model on the target speaker's voice data to generate new speech content in their voice. Both techniques have applications in speech synthesis, but voice conversion focuses on modifying existing speech, while voice cloning generates new speech content.
Why do we need voice conversion?
Voice conversion has several practical applications, including: 1. Personalizing text-to-speech systems: By converting synthesized speech to a user's preferred voice, voice conversion can make interactions with digital assistants and other applications more engaging and enjoyable. 2. Entertainment industry: Voice conversion can be used in movies, animations, and video games to create unique character voices or dubbing in different languages. 3. Accessibility: For individuals with speech impairments, voice conversion can improve communication by converting their speech into a more intelligible voice.
How do you convert letters to voice?
Converting letters or text to voice is a process called text-to-speech (TTS) synthesis. TTS systems use natural language processing and speech synthesis techniques to generate human-like speech from written text. These systems typically involve two main components: a text analysis module that converts the input text into a phonetic representation, and a speech synthesis module that generates the speech waveform from the phonetic representation.
Is there a program that mimics voice?
Yes, there are several programs and machine learning models that can mimic or clone a person's voice. These systems typically require a dataset of the target speaker's voice to train the model. Once trained, the model can generate new speech content in the target speaker's voice. Examples of such systems include Google's Tacotron, Baidu's Deep Voice, and OpenAI's WaveNet.
What are the main challenges in voice conversion research?
The main challenges in voice conversion research include: 1. Non-parallel data: The absence of corresponding pairs of source and target speaker utterances makes it difficult to train models for voice conversion. 2. Noisy training data: The presence of noise in the training data can degrade the performance of voice conversion systems. 3. Zero-shot voice style transfer: Generating voices for previously unseen speakers is a challenging task that requires advanced techniques and models.
How does CycleGAN work in voice conversion?
CycleGAN (Cycle-Consistent Adversarial Networks) is a popular approach for voice conversion that does not require parallel training data. It consists of two generator networks and two discriminator networks. The generators learn to convert the source speaker's voice to the target speaker's voice and vice versa, while the discriminators learn to distinguish between real and converted voices. The cycle consistency loss ensures that the converted voice, when converted back to the original speaker's voice, closely resembles the original input. This approach has shown promising results in one-to-one voice conversion tasks.
Can voice conversion be used for speaker verification?
Voice conversion can potentially be used to improve speaker verification systems by generating additional training data for the target speaker. However, it can also pose a security risk, as malicious actors may use voice conversion techniques to impersonate a target speaker and bypass speaker verification systems. Therefore, it is crucial to develop robust countermeasures to detect and prevent such attacks.
What is the Invertible Voice Conversion framework (INVVC)?
The Invertible Voice Conversion (INVVC) framework is an approach for voice conversion that allows for traceability of the source identity. It can be applied to one-to-one and many-to-one voice conversion tasks using parallel training data. INVVC uses an invertible neural network to learn a mapping between the source and target speaker's voice features. This invertible property enables the recovery of the original source speaker's identity from the converted voice, which can be useful in applications where preserving the source identity is important.
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