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+synthesis.+Despite+the+ongoing+rush+towards+scaling+paradigms%252C+audio+tokenization+ironically+amplifies+the+scalability+challenge%252C+stemming+from+its+long+sequence+length+and+the+complexity+of+modelling+the+multiple+sequences.+To+mitigate+these+issues%252C+we+present+CLaM-TTS+that+employs+a+probabilistic+residual+vector+quantization+to+(1)+achieve+superior+compression+in+the+token+length%252C+and+(2)+allow+a+language+model+to+generate+multiple+tokens+at+once%252C+thereby+eliminating+the+need+for+cascaded+modeling+to+handle+the+number+of+token+streams.+Our+experimental+results+demonstrate+that+CLaM-TTS+is+better+than+or+comparable+to+state-of-the-art+neural+codec-based+TTS+models+regarding+naturalness%252C+intelligibility%252C+speaker+similarity%252C+and+inference+speed.+In+addition%252C+we+examine+the+impact+of+the+pretraining+extent+of+the+language+models+and+their+text+tokenization+strategies+on+performances.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.566d75ef6a7477a88776bf3d40139dc5%3frik%3dhejfhlaUcMW7hA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Lif+Iif+TTS+Neural+Encoding+Schemes&simid=608019812342920716&FORM=IRPRST&ck=C4EC5D71CA23C77A783A54FB8EF4DCD8&selectedIndex=8&itb=0)
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