Phonological-based　features　(articulatory　features,　AFs)　describe　the　movements　of　the　vocal　organ　which　are　shared　across　languages.　This　paper　investigates　a　domain-adversarial　neural　network　(DANN)　to　extract　reliable　AFs,　and　different　multi-stream　techniques　are　used　for　cross-lingual　speech　recognition.　First,　a　novel　universal　phonological　attributes　definition　is　proposed　for　Mandarin,　English,　German　and　French.　Then　a　DANN-based　AFs　detector　is　trained　using　source　languages　(English,　German　and　French).　When　doing　the　cross-lingual　speech　recognition,　the　AFs　detectors　are　used　to　transfer　the　phonological　knowledge　from　source　languages　(English,　German　and　French)　to　the　target　language　(Mandarin).　Two　multi-stream　approaches　are　introduced　to　fuse　the　acoustic　features　and　cross-lingual　AFs.　In　addition,　the　monolingual　AFs　system　(i.e.,　the　AFs　are　directly　extracted　from　the　target　language)　is　also　investigated.　Experiments　show　that　the　performance　of　the　AFs　detector　can　be　improved　by　using　convolutional　neural　networks　(CNN)　with　a　domain-adversarial　learning　method.　The　multi-head　attention　(MHA)　based　multi-stream　can　reach　the　best　performance　compared　to　the　baseline,　cross-lingual　adaptation　approach,　and　other　approaches.　More　specifically,　the　MHA-mode　with　cross-lingual　AFs　yields　significant　improvements　over　monolingual　AFs　with　the　restriction　of　training　data　size　and,　which　can　be　easily　extended　to　other　low-resource　languages.