  The neural codec model reduces speech data transmission delay and serves as
the foundational tokenizer for speech language models (speech LMs). Preserving
emotional information in codecs is crucial for effective communication and
context understanding. However, there is a lack of studies on emotion loss in
existing codecs. This paper evaluates neural and legacy codecs using subjective
and objective methods on emotion datasets like IEMOCAP. Our study identifies
which codecs best preserve emotional information under various bitrate
scenarios. We found that training codec models with both English and Chinese
data had limited success in retaining emotional information in Chinese.
Additionally, resynthesizing speech through these codecs degrades the
performance of speech emotion recognition (SER), particularly for emotions like
sadness, depression, fear, and disgust. Human listening tests confirmed these
findings. This work guides future speech technology developments to ensure new
codecs maintain the integrity of emotional information in speech.
