Chinese is a tonal language that uses fundamental frequency, in addition to phones for word differentiation. Commonly used front-end features, such as Mel-Frequency Cepstral Coefficients (MFCC), however, are optimized for non-tonal languages such as English and are not mainly focused on pitch information that is important for tone identification. In this paper, we examine the integration of tone-related acoustic features for Chinese recognition. We propose the use of Cepstrum Method (CEP), which uses the same configurations as in MFCC extraction, for the extraction of pitch-related features. The pitch periods extracted from the CEP algorithm can be used directly for speech recognition and do not require any special treatment for unvoiced frames. In addition, we explore a number of feature transformations and find that the addition of a properly normalized and transformed set of pitch related-features can reduce the recognition error rate from 34.61% to 29.45% on the Chinese 1998 National Performance Assessment (Project 863) corpus.