Abstract: A set of benchmark text strings may be classified to provide a set of benchmark classifications. The benchmark text strings in the set may correspond to a benchmark corpus of benchmark utterances in a particular language. A benchmark classification distribution of the set of benchmark classifications may be determined. A respective classification for each text string in a corpus of text strings may also be determined. Text strings from the corpus of text strings may be sampled to form a training corpus of training text strings such that the classifications of the training text strings have a training text string classification distribution that is based on the benchmark classification distribution. The training corpus of training text strings may be used to train an automatic speech recognition (ASR) system.

Abstract: Techniques are disclosed for securely classifying or decoding data. By way of example, a method of determining a most likely sequence for a given data set comprises a computer system associated with a first party performing the following steps. An encrypted model is obtained from a second party. The encrypted model is utilized to determine cost values associated with a particular sequence of observed outputs associated with the given data set. The cost values are sent to the second party. At least one index of a minimum cost value determined by the second party from the cost values sent thereto is obtained from the second party. A minimum cost sequence resulting from the at least one index is determined as the most likely sequence.

Abstract: A set of benchmark text strings may be classified to provide a set of benchmark classifications. The benchmark text strings in the set may correspond to a benchmark corpus of benchmark utterances in a particular language. A benchmark classification distribution of the set of benchmark classifications may be determined. A respective classification for each text string in a corpus of text strings may also be determined. Text strings from the corpus of text strings may be sampled to form a training corpus of training text strings such that the classifications of the training text strings have a training text string classification distribution that is based on the benchmark classification distribution. The training corpus of training text strings may be used to train an automatic speech recognition (ASR) system.

Abstract: A set of benchmark text strings may be classified to provide a set of benchmark classifications. The benchmark text strings in the set may correspond to a benchmark corpus of benchmark utterances in a particular language. A benchmark classification distribution of the set of benchmark classifications may be determined. A respective classification for each text string in a corpus of text strings may also be determined. Text strings from the corpus of text strings may be sampled to form a training corpus of training text strings such that the classifications of the training text strings have a training text string classification distribution that is based on the benchmark classification distribution. The training corpus of training text strings may be used to train an automatic speech recognition (ASR) system.

Abstract: Techniques are disclosed for securely classifying or decoding data. By way of example, a method of determining a most likely sequence for a given data set comprises a computer system associated with a first party performing the following steps. An encrypted model is obtained from a second party. The encrypted model is utilized to determine cost values associated with a particular sequence of observed outputs associated with the given data set. The cost values are sent to the second party. At least one index of a minimum cost value determined by the second party from the cost values sent thereto is obtained from the second party. A minimum cost sequence resulting from the at least one index is determined as the most likely sequence.