Voice Conversion Challenges

[mahdeslami11]

Online signal processing systems always have multiple hardware, software, and algorithmic limitations. which are generally solved with the advancement of technology. But in special applications, for example in Multilingual Voice conversion systems in several, the both of database and system model seems very important. In your opinion, considering future markets, what are the most important challenges facing online Voice conversion systems?

[Najafi2022]

The challenge of language, accent, and dialect coverage.
There are more than 7000 languages spoken in the world, with an uncountable number of accents and dialects. English alone has more than 160 dialects spoken all over the world.

[tesla568]

The ability of new handsets to understand different accents.

[sanagolmarzi]

Speaker identity is one of the important characteristics of human speech. In voice conversion, we change the speaker identity from one to another, while keeping the linguistic content unchanged. Voice conversion involves multiple speech processing techniques, such as speech analysis, spectral conversion, prosody conversion, speaker characterization, and vocoding. With the recent advances in theory and practice, we are now able to produce human-like voice quality with high speaker similarity. In this paper, we provide a comprehensive overview of the state-of-the-art of voice conversion techniques and their performance evaluation methods from the statistical approaches to deep learning, and discuss their promise and limitations. We will also report the recent Voice Conversion Challenges (VCC), the performance of the current state of technology, and provide a summary of the available resources for voice conversion research.

[soorenalotfiarjmand]

According to a recent survey, 73% of respondents claimed that accuracy was the biggest hindrance in adopting speech recognition tech.
Imprecision and false interpretations. Speech recognition software isn't always able to interpret spoken words correctly. This is due to computers not being on par with humans in understanding the contextual relation of words and sentences, causing misinterpretations of what the speaker meant to say or achieve.

[alixahedi]

The word recognition challenge, as noted by @Najafi2022 The dialect of a particular word may be different by different people, and if we need to store several different dialects in the system for each word, we will need very large databases.

[Nargesrezaeei]

Challenges ahead can include audio samples that are of low quality. Also, variations in the voice of users due to illnesses such as colds or mood changes, strong background noise as a caller with a guard system to verify identity and changes in call transfer technology are among the things that can affect speech recognition. It affects the time of identification.

[WillamSun]

Challenges would include voice sampling. We will need hundreds of different voices from hundreds of different people from anywhere on the earth for training computers.
As @Najafi2022 said, we will need a large database, and also we need hundreds of people to take part in it.

[Shabnamlatifian]

The challenge of accuracy. The accuracy of a speech recognition system (SRS) needs to be high if it is to create any value. ...
The challenge of language, accent, dialect coverage. ...
The challenge of security and privacy. ...
The challenge of cost and deployment.

[WillamSun]

Agree

[narges1998akl]

One of the upcoming challenges is definitely the accent or phonetic type that people use in words

[alirezaahmadiii]

The current challenges of speech recognition are caused by two major factors – reach and loud environments. This calls for even more precise systems that can tackle the most ambitious ASR use-cases. Think about live interviews, speech recognition at a loud family dinner or meetings with various people. These are the upcoming challenges to be solved for next-gen voice recognition.
Beyond this, speech recognition needs to be made available for more languages and cover wide topics. Because as of now, [ASR needs a lot of data to work well] and some of it just hasn’t been collected for certain languages and topics. Without adding these, ASR systems will remain noticeably handicapped.

[Pegahshafiei]

One of its challenges is a word, sentence, or proverb that, for example, has a special meaning in an area

[yaassinn]

In voice conversion, we change the
speaker identity from one to another, while keeping the lin-
guistic content unchanged. Voice conversion involves multiple
speech processing techniques, such as speech analysis, spectral
conversion, prosody conversion, speaker characterization, and
vocoding. With the recent advances in theory and practice, we
are now able to produce human-like voice quality with high
speaker similarity. In this paper, we provide a comprehensive
overview of the state-of-the-art of voice conversion techniques
and their performance evaluation methods from the statistical
approaches to deep learning, and discuss their promise and
limitations. We will also report the recent Voice Conversion
Challenges (VCC).....here is the link: ((https://arxiv.org/pdf/2008.03648.pdf))

[Hanafj]

Perhaps if I want to point out the challenges and obstacles facing voice conversation technology, I can say that this technology is highly dependent on being online and if we are not connected to the Internet, we cannot practically use this technology and its benefits. The solution is that we can use Here, a database or the creation of a specific algorithm solved the dependence on the Internet in this field. On the other hand, despite all the advances that this technology has made, it still has problems in fully understanding the words, which can be improved with a series of improvements.

[ahoora988]

One of the very important challenges that can be pointed out about voice conversation technology is that in the first step, the dependence on the Internet that we see is a bit annoying that a complete database should be considered in this regard. The next important point It is that some words and terms are terms that are only used in that region. Also, some words have the same sounds and with a series of improvements in this field, we can see great progress in this field.

[Panizeslami99]

these ideas have assumed great importance in the design of sophisticated modern
communications systems. Therefore, even though our main focus will
be mostly on the speech waveform and its representation in the form of
parametric models, it is nevertheless useful to begin with a discussion
of how information is encoded in the speech waveform.

[Zahrakarbalaeimohammadi]

In this field, we are facing challenges such as personalization, accuracy, speech recognition, languages ​​and special topics, etc.

[seyedmahdifallahi]

The most important challenge is the existence of many different languages and dialects that exist in different parts of the world, and after obtaining this information, we certainly have a large amount of data, which takes a lot of time to process for the system.

[mahdigodarzi]

The voice conversion challenge is a bi-annual scientific event held to compare and understand different voice conversion (VC) systems built on a common dataset. In 2020, we organized the third edition of the challenge and constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. After a two-month challenge period, we received 33 submissions, including 3 baselines built on the database. From the results of crowd-sourced listening tests, we observed that VC methods have progressed rapidly thanks to advanced deep learning methods. In particular, speaker similarity scores of several systems turned out to be as high as target speakers in the intra-lingual semi-parallel VC task. However, we confirmed that none of them have achieved human-level naturalness yet for the same task. The cross-lingual conversion task is, as expected, a more difficult task, and the overall naturalness and similarity scores were lower than those for the intra-lingual conversion task. However, we observed encouraging results, and the MOS scores of the best systems were higher than 4.0. We also show a few additional analysis results to aid in understanding cross-lingual VC better.

[Fatemehmojaver]

One of the most important challenges in this field is the variety of dialects and the requirement to be online

[SepidehKianasab]

Background noise
While trying to improve the accuracy of a speech recognition model, background noise can be a significant barrier. When the system is exposed to the real world, there are a lot of background noises, such as cross-talk, white noises, and other distortions that can disrupt the SRS.

Field specificity
Field-specific terms and jargon can also cause hindrances to the SRS’s accuracy. For instance, complicated medical or legal terms can be difficult for the model to understand and can further decrease its accuracy

[Maryammsv]

Peripheral background sounds
Another top speech recognition problem that needs a solution is – noise. It is everywhere! And so, it becomes the job of the ASR solution to accurately catch the speech input through unwanted sounds. An ASR should be able to pick up the input’s sound waves even from a distance in a room riddled with white noise and cross-talk. Echo, for example, also adds to the imprecision. Reflected sound waves from surfaces in the space distort the receptor’s ability to process the actual input unerringly.

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