18. The non-transitory computer readable storage medium of claim 16 , wherein the one or more programs comprising instructions, which further cause the device to: collect the plurality of audio samples in the plurality of different languages and labeled data for the plurality of audio samples; obtain a phoneme collection for each of the plurality of different languages; map phonemes from each phoneme collection to phonemes in the IPA so as to generate the IPA phoneme mapping collection; and wherein the acoustic model is trained based on the collected plurality of audio samples in the plurality of different languages, the collected labeled data for the plurality of audio samples, and the generated IPA phoneme mapping collection.
16. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which, when executed by an electronic device with one or more processors, cause the device to: train an acoustic model with an International Phonetic Alphabet (IPA) phoneme mapping collection and a plurality of audio samples in a plurality of different languages, wherein the acoustic model includes: a foreground model configured to match a phoneme in an input audio signal to a corresponding keyword, wherein the foreground model is trained by (i) obtaining a phoneme collection for each of the plurality of different languages, (ii) generating a plurality of triphones by linking phonemes in the phoneme collection corresponding to the language, and (iii) performing Gaussian splitting training on the triphones that are clustered with a decision tree corresponding to the language; and a background model configured to match a phoneme in the input audio signal to a corresponding non-keyword; after training the acoustic model, generate a phone decoder based on the trained acoustic model; obtain a keyword phoneme sequence for a respective keyword in a respective language of the plurality of different languages, wherein the obtaining includes: collecting a set of keyword audio samples for the respective keyword in the respective language; decoding the set of keyword audio samples with the phone decoder to generate a set of phoneme sequence candidates for the respective keyword, each phoneme sequence candidate corresponding to a respective keyword audio sample; and selecting the keyword phoneme sequence for the respective keyword from the set of phoneme sequence candidates by choosing a phoneme of a highest confidence measure from one of the set of phoneme sequence candidates at each location in the corresponding sequence and assembling the chosen phonemes into the keyword phoneme sequence according to their locations in the corresponding sequence; after obtaining the keyword phoneme sequence, detect one or more keywords in the input audio signal with the trained acoustic model, wherein the detecting includes: matching one or more phonemic keyword portions of the input audio signal with one or more phonemes in the keyword phoneme sequence with the foreground model; and filtering out one or more phonemic non-keyword portions of the input audio signal with the background model.