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+has+led+to+significant+success+in+text-to-speech+(TTS)+synthesis.+Although+these+models+can+generate+speech+with+high+quality+and+naturalness%252C+their+synthesised+samples+can+still+suffer+from+artefacts%252C+mispronunciation%252C+word+repeating%252C+etc.+In+this+paper%252C+we+argue+these+undesirable+properties+could+partly+be+caused+by+the+randomness+of+sampling-based+strategies+during+the+autoregressive+decoding+of+LMs.+Therefore%252C+we+look+at+maximisation-based+decoding+approaches+and+propose+Temporal+Repetition+Aware+Diverse+Beam+Search+(TRAD-BS)+to+find+the+most+probable+sequences+of+the+generated+speech+tokens.+Experiments+with+two+state-of-the-art+LM-based+TTS+models+demonstrate+that+our+proposed+maximisation-based+decoding+strategy+generates+speech+with+fewer+mispronunciations+and+improved+speaker+consistency.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.b9aa332630d2048d54cf889f0efe6a29%3frik%3dAvHr8N7ZqBVcKg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Beam+Search+Language+Model&FORM=IRPRST&ck=7B9947AB4A508B831D2F580CE46F15D3&selectedIndex=1&itb=0)
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