13. An apparatus for measuring the quality of speech communicated using packets, comprising: a plurality of N independent neural networks, each neural network being configured to process one of N separate sets of speech signal feature measurements corresponding to a speech signal recovered from packets communicated via a communications network to generate from the processed set of speech signal feature measurements a signal feature quality score, each of the N different neural networks corresponding to a different one of the N sets of speech signal feature measurements, said N neural networks generating N signal feature quality scores, each one of the N signal feature quality scores corresponding to a different one of N speech signal features; a joint quality score determination neural network configured to generate a joint signal feature quality score from said N signal feature quality scores; a score adjustment module configured to adjust said joint quality score based on packet loss information and codec type to generate a final overall quality score for said recovered speech signal wherein different types of codecs correspond to different packet loss robustness factors; and wherein said N sets of separate speech signal feature measurements include at least two sets of speech signal measurements from a group of sets of speech signal feature measurements including: a set of signal to noise ratio measurements, a set of spectral clarity measurements, a set of linear prediction skew measurements, a set of linear prediction kurtosis measurements and a set of pitch average measurements.
13. An apparatus for measuring the quality of speech communicated using packets, comprising: a plurality of N independent neural networks, each neural network being configured to process one of N separate sets of speech signal feature measurements corresponding to a speech signal recovered from packets communicated via a communications network to generate from the processed set of speech signal feature measurements a signal feature quality score, each of the N different neural networks corresponding to a different one of the N sets of speech signal feature measurements, said N neural networks generating N signal feature quality scores, each one of the N signal feature quality scores corresponding to a different one of N speech signal features; a joint quality score determination neural network configured to generate a joint signal feature quality score from said N signal feature quality scores; a score adjustment module configured to adjust said joint quality score based on packet loss information and codec type to generate a final overall quality score for said recovered speech signal wherein different types of codecs correspond to different packet loss robustness factors; and wherein said N sets of separate speech signal feature measurements include at least two sets of speech signal measurements from a group of sets of speech signal feature measurements including: a set of signal to noise ratio measurements, a set of spectral clarity measurements, a set of linear prediction skew measurements, a set of linear prediction kurtosis measurements and a set of pitch average measurements. 14. The apparatus of claim 13 , wherein said plurality of N neural networks includes at least a first neural network corresponding to a first speech signal feature and a second neural network corresponding to a second speech signal feature; wherein said N sets of speech feature signal measurements include a first set of speech signal feature measurements generated from recovered speech signal and a second set of speech signal feature measurements generated from recovered speech signal; and wherein said first neural network is configured to generate a first signal feature quality score from the first set of speech signal feature measurements, and said second neural network is configured to generate a second signal feature quality score from the second set of speech signal feature measurements.