Conventional wisdom in automatic speech recognition assertsthat pitch information is not helpful in building speech recognizers for non-tonal languages and contributes only modestly to performance in speech recognizers for tonal languages.To maintain consistency between different systems, pitch is therefore often ignored, trading the slight performance benefits for greater system uniformity/simplicity. In thispaper, we report results that challenge this conventional approach. We present new models of tone that deliver consistent performance improvements for tonal languages (Cantonese,Vietnamese) and even modest improvements for non-tonal languages. Using neural networks for feature integration and fusion, these models achieve significant gains throughout, and provide us with system uniformity and standardization across all languages, tonal and non-tonal.